Speech recognition adaptation systems based on adaptation data

ABSTRACT

Computationally implemented methods and systems include receiving indication of initiation of a speech-facilitated transaction between a party and a target device, and receiving adaptation data correlated to the party. The receiving is facilitated by a particular device associated with the party. The adaptation data is at least partly based on previous adaptation data derived at least in part from one or more previous speech interactions of the party. The methods and systems also include applying the received adaptation data correlated to the party to the target device, and processing speech from the party using the target device to which the received adaptation data has been applied. In addition to the foregoing, other aspects are described in the claims, drawings, and text.

BACKGROUND

This application is related portable speech adaptation data.

SUMMARY

A computationally implemented method includes, but is not limited to,receiving indication of initiation of a speech-facilitated transactionbetween a particular party and a target device, receiving adaptationdata correlated to the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party, applying the received adaptation datacorrelated to the particular party to the target device, and processingspeech from the particular party using the target device to which thereceived adaptation data has been applied. In addition to the foregoing,other method aspects are described in the claims, drawings, and textforming a part of the present disclosure.

In one or more various aspects, related systems include but are notlimited to circuitry and/or programming for effecting the hereinreferenced method aspects; the circuitry and/or programming can bevirtually any combination of hardware, software, and/or firmware in oneor more machines or article of manufacture configured to effect theherein-referenced method aspects depending upon the design choices ofthe system designer.

A computationally-implemented system includes, but is not limited to,means for receiving indication of initiation of a speech-facilitatedtransaction between a particular party and a target device, means forreceiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party, means for applyingthe received adaptation data correlated to the particular party to thetarget device, and means for processing speech from the particular partyusing the target device to which the received adaptation data has beenapplied.

A computationally-implemented system includes, but is not limited to,circuitry for receiving indication of initiation of a speech-facilitatedtransaction between a particular party and a target device, circuitryfor receiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party, circuitry forapplying the received adaptation data correlated to the particular partyto the target device, and circuitry for processing speech from theparticular party using the target device to which the receivedadaptation data has been applied.

A computer program product comprising an article of manufacture bearsinstructions including, but not limited to, one or more instructions forreceiving indication of initiation of a speech-facilitated transactionbetween a particular party and a target device, one or more instructionsfor receiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party, one or moreinstructions for applying the received adaptation data correlated to theparticular party to the target device, and one or more instructions forprocessing speech from the particular party using the target device towhich the received adaptation data has been applied.

A computationally-implemented method that specifies that a plurality oftransistors and/or switches reconfigure themselves into a machine thatcarries out the following including, but not limited to, receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device, receiving adaptation datacorrelated to the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party, applying the received adaptation datacorrelated to the particular party to the target device, and processingspeech from the particular party using the target device to which thereceived adaptation data has been applied.

A computer architecture comprising at least one level, comprisingarchitecture configured to be receiving indication of initiation of aspeech-facilitated transaction between a particular party and a targetdevice, architecture configured to be receiving adaptation datacorrelated to the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party, architecture configured to be applying thereceived adaptation data correlated to the particular party to thetarget device, and architecture configured to be processing speech fromthe particular party using the target device to which the receivedadaptation data has been applied.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1, including FIGS. 1A and 1B, shows a high-level block diagram of aterminal device 30 operating in an exemplary environment 100, accordingto an embodiment.

FIG. 2, including FIGS. 2A-2B, shows a particular perspective of thespeech-facilitated transaction initiation between particular party andtarget device indicator receiving module 52 of the terminal device 30 ofenvironment 100 of FIG. 1.

FIG. 3, including FIGS. 3A-3K, shows a particular perspective of theparticular party-correlated previous speech interaction based adaptationdata from particular-party associated particular device receiving module54 of the terminal device 30 of environment 100 of FIG. 1.

FIG. 4, including FIGS. 4A-4C, shows a particular perspective of thereceived adaptation data to target device applying module 56 of theterminal device 30 of environment 100 of FIG. 1.

FIG. 5, including FIGS. 5A-5C, shows a particular perspective of thetarget device particular party speech processing using receivedadaptation data module 58 of the terminal device 30 of environment 100of FIG. 1.

FIG. 6 is a high-level logic flowchart of a process, e.g., operationalflow 600, according to an embodiment.

FIG. 7A is a high-level logic flowchart of a process depicting alternateimplementations of an indication of initiation receiving operation 502of FIG. 6.

FIG. 7B is a high-level logic flowchart of a process depicting alternateimplementations of the indication of initiation receiving operation 502of FIG. 6.

FIG. 7C is a high-level logic flowchart of a process depicting alternateimplementations of the indication of initiation receiving operation 502of FIG. 6.

FIG. 8A is a high-level logic flowchart of a process depicting alternateimplementations of an adaptation data receiving operation 504 of FIG. 6.

FIG. 8B is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8C is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8D is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8E is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8F is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8G is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8H is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8I is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8J is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8K is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8L is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8M is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8N is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 8P is a high-level logic flowchart of a process depicting alternateimplementations of the adaptation data receiving operation 504 of FIG.6.

FIG. 9A is a high-level logic flowchart of a process depicting alternateimplementations of a received adaptation data applying operation 506 ofFIG. 6.

FIG. 9B is a high-level logic flowchart of a process depicting alternateimplementations of the received adaptation data applying operation 506of FIG. 6.

FIG. 9C is a high-level logic flowchart of a process depicting alternateimplementations of the received adaptation data applying operation 506of FIG. 6.

FIG. 10A is a high-level logic flowchart of a process depictingalternate implementations of a speech processing operation 508 of FIG.6.

FIG. 10B is a high-level logic flowchart of a process depictingalternate implementations of the speech processing operation 508 of FIG.6.

FIG. 10C is a high-level logic flowchart of a process depictingalternate implementations of the speech processing operation 508 of FIG.6.

FIG. 10D is a high-level logic flowchart of a process depictingalternate implementations of the speech processing operation 508 of FIG.6.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar or identical components oritems, unless context dictates otherwise. The illustrative embodimentsdescribed in the detailed description, drawings, and claims are notmeant to be limiting. Other embodiments may be utilized, and otherchanges may be made, without departing from the spirit or scope of thesubject matter presented here.

The proliferation of automation in many transactions is apparent. Forexample, Automated Teller Machines (“ATMs”) dispense money and receivedeposits. Airline ticket counter machines check passengers in, dispensetickets, and allow passengers to change or upgrade flights. Train andsubway ticket counter machines allow passengers to purchase a ticket toa particular destination without invoking a human interaction at all.Many groceries and pharmacies have self service checkout machines whichallow a consumer to pay for goods purchased by interacting only with amachine. Large companies now staff telephone answering systems withmachines that interact with customers, and invoke a human in thetransaction only if there is a problem with the machine-facilitatedtransaction.

Nevertheless, as such automation increases, convenience andaccessibility may decrease. Self-checkout machines at grocery stores maybe difficult to operate. ATMs and ticket counter machines may be mostlyinaccessible to disabled persons or persons requiring special access.Where before, the interaction with a human would allow disabled personsto complete transactions with relative ease, if a disabled person isunable to push the buttons on an ATM, there is little the machine can doto facilitate the transaction to completion. While some of these publicterminals allow speech operations, they are configured to the mostgeneric forms of speech, which may be less useful in recognizingparticular speakers, thereby leading to frustration for users attemptingto speak to the machine. This problem may be especially challenging forthe disabled, who already may face significant challenges in completingtransactions with automated machines.

In addition, smartphones and tablet devices also now are configured toreceive speech commands. Speech and voice controlled automobile systemsnow appear regularly in motor vehicles, even in economical,mass-produced vehicles. Home entertainment devices, e.g., disc players,televisions, radios, stereos, and the like, may respond to speechcommands. Additionally, home security systems may respond to speechcommands. In an office setting, a worker's computer may respond tospeech from that worker, allowing faster, more efficient work flows.Such systems and machines may be trained to operate with particularusers, either through explicit training or through repeatedinteractions. Nevertheless, when that system is upgraded or replaced,e.g., a new TV is bought, that training may be lost with the device.

Thus, adaptation data for speech recognition systems may be separatedfrom the device which recognizes the speech, and may be more closelyassociated with a user, e.g., through a device carried by the user, orthrough a network location associated with the user. In accordance withvarious embodiments, computationally implemented methods, systems,circuitry, articles of manufacture, and computer program products aredesigned to, among other things, provide an interface for receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device, receiving adaptation datacorrelated to the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party, applying the received adaptation datacorrelated to the particular party to the target device, and processingspeech from the particular party using the target device to which thereceived adaptation data has been applied.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

Referring now to FIG. 1, FIG. 1 illustrates an example environment 100in which the methods, systems, circuitry, articles of manufacture, andcomputer program products and architecture, in accordance with variousembodiments, may be implemented by terminal device 30. The terminaldevice 30, in various embodiments, may be endowed with logic that isdesigned for receiving indication of initiation of a speech-facilitatedtransaction between a particular party and a target device, receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party, applying the receivedadaptation data correlated to the particular party to the target device,and processing speech from the particular party using the target deviceto which the received adaptation data has been applied.

Referring again to the exemplary embodiment 100 of FIG. 1, a user 5 mayengage in a speech-facilitated transaction with a terminal device 30.Terminal device 30 may include a microphone 22 and a screen 23. In someembodiments, screen 23 may be a touchscreen. Although FIG. 1A depictsterminal device 30 as a terminal for simplicity of illustration,terminal device 30 could be any device that is configured to receivespeech. For example, terminal device 30 may be a terminal, a computer, anavigation system, a phone, a piece of home electronics (e.g., a DVDplayer, Blu-Ray player, media player, game system, television, receiver,alarm clock, and the like). Terminal device 30 may, in some embodiments,be a home security system, a safe lock, a door lock, a kitchen applianceconfigured to receive speech, and the like. In some embodiments,terminal device 30 may be a motorized vehicle, e.g., a car, boat,airplane, motorcycle, golf cart, wheelchair, and the like. In someembodiments, terminal device 30 may be a piece of portable electronics,e.g., a laptop computer, a netbook computer, a tablet device, asmartphone, a cellular phone, a radio, a portable navigation system, orany other piece of electronics capable of receiving speech. Terminaldevice 30 may be a part of an enterprise solution, e.g., a commonworkstation in an office, a copier, a scanner, a personal workstation ina cubicle, an office directory, an interactive screen, and a telephone.These examples and lists are not meant to be exhaustive, but merely toillustrate a few examples of the terminal device.

In an embodiment, personal device 20 may facilitate the transmission ofadaptation data to the terminal 30. In FIG. 1A, personal device 20 isshown as a phone-type device that fits into pocket 5A of the user.Nevertheless, in other embodiments, personal device 20 may be any sizeand have any specification. Personal device 20 may be a custom device ofany shape or size, configured to transmit, receive, and store data.Personal device 20 may include, but is not limited to, a smartphonedevice, a tablet device, a personal computer device, a laptop device, akeychain device, a key, a personal digital assistant device, a modifiedmemory stick, a universal remote control, or any other piece ofelectronics. In addition, personal device 20 may be a modified objectthat is worn, e.g., eyeglasses, a wallet, a credit card, a watch, achain, or an article of clothing. Anything that is configured to store,transmit, and receive data may be a personal device 20, and personaldevice 20 is not limited in size to devices that are capable of beingcarried by a user. Additionally, personal device 20 may not be in directproximity to the user, e.g., personal device 20 may be a computersitting on a desk in a user's home or office.

In some embodiments, terminal 30 receives adaptation data from thepersonal device 20, in a process that will be described in more detailherein. In some embodiments, the adaptation data is transmitted over oneor more communication network(s) 40. In various embodiments, thecommunication network 40 may include one or more of a local area network(LAN), a wide area network (WAN), a metropolitan area network (MAN), awireless local area network (WLAN), a personal area network (PAN), aWorldwide Interoperability for Microwave Access (WiMAX), public switchedtelephone network (PTSN), a general packet radio service (GPRS) network,a cellular network, and so forth. The communication networks 40 may bewired, wireless, or a combination of wired and wireless networks. It isnoted that “communication network” here refers to one or morecommunication networks, which may or may not interact with each other.

In some embodiments, the adaptation data does not come directly from thepersonal device 20. In some embodiments, personal device 20 merelyfacilitates communication of the adaptation data, e.g., by providing oneor more of an address, credentials, instructions, authorization, andrecommendations. For example, in some embodiments, personal device 20provides a location at server 10 at which adaptation data may bereceived. In some embodiments, personal device 20 retrieves adaptationdata from server 10 upon a request from the terminal device 30, and thenrelays or facilitates in the relaying of the adaptation data to terminaldevice 30.

In some embodiments, personal device 20 broadcasts the adaptation dataregardless of whether a terminal device 30 is listening, e.g., atpredetermined, regular, or otherwise-defined intervals. In otherembodiments, personal device 20 listens for a request from a terminaldevice 30, and transmits or broadcasts adaptation data in response tothat request. In some embodiments, user 5 determines when personaldevice 20 broadcasts adaptation data. In still other embodiments, athird party (not shown) triggers the transmission of adaptation data tothe terminal device 30, in which the transmission is facilitated by thepersonal device 20.

Referring again to the exemplary environment 100 depicted in FIG. 1, invarious embodiments, the terminal device 30 may comprise, among otherelements, a processor 32, a memory 34, and a user interface 35.Processor 32 may include one or more microprocessors, Central ProcessingUnits (“CPU”), a Graphics Processing Units (“GPU”), Physics ProcessingUnits, Digital Signal Processors, Network Processors, Floating PointProcessors, and the like. In some embodiments, processor 32 may be aserver. In some embodiments, processor 32 may be a distributed-coreprocessor. Although processor 32 is depicted as a single processor thatis part of a single computing device 30, in some embodiments, processor32 may be multiple processors distributed over one or many computingdevices 30, which may or may not be configured to work together.Processor 32 is illustrated as being configured to execute computerreadable instructions in order to execute one or more operationsdescribed above, and as illustrated in FIGS. 6, 7A-7C, 8A-8P, 9A-9C, and10A-10D. In some embodiments, processor 32 is designed to be configuredto operate as processing module 50, which may include speech-facilitatedtransaction initiation between particular party and target deviceindicator receiving module 52, particular party-correlated previousspeech interaction based adaptation data from particular-partyassociated particular device receiving module 54, received adaptationdata to target device applying module 56, and target device particularparty speech processing using received adaptation data module 58.

Referring again to the exemplary environment 100 of FIG. 1, terminaldevice 30 may comprise a memory 34. In some embodiments, memory 34 maycomprise of one or more of one or more mass storage devices, read-onlymemory (ROM), programmable read-only memory (PROM), erasableprogrammable read-only memory (EPROM), cache memory such as randomaccess memory (RAM), flash memory, synchronous random access memory(SRAM), dynamic random access memory (DRAM), and/or other types ofmemory devices. In some embodiments, memory 34 may be located at asingle network site. In other embodiments, memory 34 may be located atmultiple network sites, including sites that are distant from eachother.

As described above, and with reference to FIG. 1, terminal device 30 mayinclude a user interface 35. The user interface may be implemented inhardware or software, or both, and may include various input and outputdevices to allow an operator of a computing device 30 to interact withcomputing device 30. For example, user interface 35 may include, but isnot limited to, an audio display, a video display, a microphone, acamera, a keyboard, a mouse, a joystick, a game controller, a touchpad,a handset, or any other device that allows interaction between acomputing device and a user. The user interface 35 also may include aspeech interface 36, which is configured to receive and/or processspeech as input.

Referring now to FIG. 2, FIG. 2 illustrates an exemplary implementationof the speech-facilitated transaction initiation between particularparty and target device indicator receiving module 52. As illustrated inFIG. 2, (e.g., FIG. 2A), the speech-facilitated transaction initiationbetween particular party and target device indicator receiving module 52may include one or more sub-logic modules in various alternativeimplementations and embodiments. For example, in some embodiments,module 52 may include speech-facilitated and partly using speechtransaction initiation between particular party and target deviceindicator receiving module 202, speech-facilitated and only using speechtransaction initiation between particular party and target deviceindicator receiving module 204, speech facilitated transaction usingspeech and terminal device button initiation indicator receiving module206, speech facilitated transaction using speech and terminal devicescreen initiation indicator receiving module 208, speech facilitatedtransaction using speech and gesture initiation indicator receivingmodule 207, and particular party intention to conduct target devicespeech-facilitated transaction indicator receiving module 210. In someembodiments, module 210 may include particular party and target deviceinteraction indication receiving module 212, particular party and targetdevice particular proximity indication receiving module 214, andparticular party and target device particular proximity and particularcondition indication receiving module 216 (e.g., which, in someembodiments, may include particular party and target device particularproximity and carrying particular device indication receiving module218.

Referring again to FIG. 2 (e.g., FIG. 2B), module 52 may includeparticular party speaking to target device indicator receiving module220, particular party intending to speak to target device indicatorreceiving module 222, speech-facilitated transaction initiation betweenparticular party and target device indicator receiving from particulardevice module 224, speech-facilitated transaction initiation betweenparticular party and target device indicator receiving from furtherdevice module 226, speech-facilitated transaction initiation betweenparticular party and target device indicator detecting module 228, andprogram configured to communicate with particular party throughspeech-facilitated transaction launch detecting module 230.

Referring now to FIG. 3, FIG. 3 illustrates an exemplary implementationof the particular party-correlated previous speech interaction basedadaptation data from particular-party associated particular devicereceiving module 54. As illustrated in FIG. 3 (e.g., FIG. 3A),particular party-correlated previous speech interaction based adaptationdata from particular-party associated particular device receiving module54 may include particular party-correlated previous speech interactionbased speech characteristics from particular-party associated particulardevice receiving module 302, particular party-correlated previous speechinteraction based instructions for adapting one or more speechrecognition modules from particular-party associated particular devicereceiving module 304, particular party-correlated previous speechinteraction based instructions for updating one or more speechrecognition modules from particular-party associated particular devicereceiving module 306, particular party-correlated previous speechinteraction based instructions for modifying one or more speechrecognition modules from particular-party associated particular devicereceiving module 308, and particular party-correlated previous speechinteraction based data linking particular party pronunciation of one ormore words to one or more words from particular-party associatedparticular device receiving module 310.

Referring again to FIG. 3 (e.g., FIG. 3B), module 54 may includeparticular party-correlated previous speech interaction based datalocating available particular party correlated adaptation data fromparticular-party associated particular device receiving module 312,particular party-correlated audibly distinguishable sound linking toconcept adaptation data from particular-party associated particulardevice receiving module 395, particular party-correlated previous speechinteraction based authorization to receive data correlated to particularparty from particular-party associated particular device receivingmodule 314, particular party-correlated previous speech interactionbased instructions for obtaining adaptation data from particular-partyassociated particular device receiving module 316, and particularparty-correlated previous speech interaction based adaptation dataincluding particular party identification data from particular-partyassociated particular device receiving module 318 (e.g., which, in someembodiments, may include particular party-correlated previous speechinteraction based adaptation data including particular party uniqueidentification data from particular-party associated particular devicereceiving module 320).

Referring again to FIG. 3 (e.g., FIG. 3C), module 54 may includeparticular party-correlated previous speech interaction based adaptationdata from particular-party owned particular device receiving module 322,particular party-correlated previous speech interaction based adaptationdata from particular-party carried particular device receiving module324, particular party-correlated previous speech interaction basedadaptation data from particular device previously used by particularparty receiving module 326, particular party-correlated previous speechinteraction based adaptation data from particular-party service contractaffiliated particular device receiving module 328, and particularparty-correlated previous speech interaction based adaptation data fromparticular device used by particular party receiving module 330.

Referring again to FIG. 3 (e.g., FIG. 3D), module 54 may includeparticular party-correlated previous speech interaction based adaptationdata particular device configured to allow particular party loginreceiving module 332, particular party-correlated previous speechinteraction based adaptation data particular device configured to storeparticular party data receiving module 334 (e.g., which, in someembodiments, may include particular party-correlated previous speechinteraction based adaptation data particular device configured to storeparticular party profile data receiving module 336 and particularparty-correlated previous speech interaction based adaptation dataparticular device configured to store particular party speech profileunrelated data receiving module 338), and particular party-correlatedprevious speech interaction based adaptation data from particular devicein particular proximity to particular party receiving module 340.

Referring again to FIG. 3 (e.g., FIG. 3E), module 54 may includeparticular party-correlated previous speech interaction based adaptationdata from particular-party associated particular device closer toparticular party receiving module 342, particular party-correlatedprevious other device speech interaction based adaptation data fromparticular-party associated particular device receiving module 344,particular party-correlated previous other related device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 346, particular party-correlatedprevious other device having same vocabulary as target device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 348, and particular party-correlatedprevious other device having same manufacturer as target device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 350.

Referring again to FIG. 3 (e.g., FIG. 3F), module 54 may includeparticular party-correlated previous other similar-function configureddevice speech interaction based adaptation data from particular-partyassociated particular device receiving module 352, particularparty-correlated previous other same-function configured device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 354, particular party-correlatedother devices previously carrying out same function as target devicespeech interaction based adaptation data from particular-partyassociated particular device receiving module 356, particularparty-correlated previous other same-type device speech interactionbased adaptation data from particular-party associated particular devicereceiving module 358, particular party-correlated previous particulardevice speech interaction based adaptation data from particular-partyassociated particular device receiving module 360, particularparty-correlated previous speech interactions observed by particulardevice based adaptation data from particular-party associated particulardevice receiving module 362, and particular party-correlated previousspeech interaction based adaptation data correlated to one or morevocabulary words and received from particular-party associatedparticular device receiving module 364 (e.g., which, in someembodiments, may include particular party-correlated previous speechinteraction based adaptation data correlated to one or more targetdevice vocabulary words and received from particular-party associatedparticular device receiving module 366.

Referring again to FIG. 3 (e.g., FIG. 3G), module 54 may includeparticular party-correlated adaptation data from particular partyassociated particular device requesting module 368 (e.g., which, in someembodiments, may include particular party-correlated adaptation datarelated to one or more vocabulary words requesting module 372) andadaptation data partly based on previous adaptation data derived fromone or more previous particular party speech interactions receivingmodule 370. In some embodiments, module 370 may include adaptation datapartly based on previous adaptation data derived from one or moreprevious particular party speech interactions with a prior devicereceiving module 386. In some embodiments, module 386 may furtherinclude adaptation data partly based on previous adaptation data derivedfrom one or more previous particular party speech interactions with acommon characteristic prior device receiving module 388. In someembodiments, module 388 may further include adaptation data partly basedon previous adaptation data derived from one or more previous particularparty speech interactions with a same function prior device receivingmodule 390. In some embodiments, module 390 may further includeadaptation data partly based on previous adaptation data derived fromone or more previous particular party speech interactions with a ticketdispenser receiving module 392.

Referring again to FIG. 3 (e.g., FIG. 3H), module 368 of module 54 mayfurther include particular party-correlated adaptation data regardingone or more target device vocabulary words requesting module 374. Insome embodiments, module 374 may further include particularparty-correlated adaptation data regarding one or more target devicecommand vocabulary words requesting module 376, particularparty-correlated adaptation data regarding one or more target devicecontrol vocabulary words requesting module 378, and particularparty-correlated adaptation data regarding one or more target deviceinteraction vocabulary words requesting module 380. In some embodiments,module 388 of module 386 of module 370 of module 54 may further includeadaptation data partly based on previous adaptation data derived fromone or more previous particular party speech interactions with a priordevice providing a same service receiving module 394. In someembodiments, module 394 may further include adaptation data partly basedon previous adaptation data derived from one or more previous particularparty speech interactions with a media player receiving module 396.

Referring again to FIG. 3 (e.g., FIG. 3I), module 368 of module 54 mayfurther include particular party-correlated adaptation data regardingone or more target device common interaction words requesting module 382and particular party-correlated adaptation data regarding one or moretarget device type associated vocabulary words requesting module 384. Insome embodiments, module 388 of module 386 of module 370 of module 54may further include adaptation data partly based on previous adaptationdata derived from one or more previous particular party speechinteractions with a prior device sold by a same entity as the targetdevice receiving module 398. In some embodiments, module 398 may includeadaptation data partly based on previous adaptation data derived fromone or more previous particular party speech interactions with a priordevice sold by a same retailer as the target device receiving module301.

Referring again to FIG. 3 (e.g., FIG. 3J), in some embodiments, module386 of module 370 of module 54 may further include adaptation datapartly based on previous adaptation data derived from one or moreprevious particular party speech interactions with a prior devicesharing at least one vocabulary word receiving module 303. Module 303may further include adaptation data partly based on previous adaptationdata derived from one or more previous particular party speechinteractions with a larger vocabulary prior device receiving module 305.In some embodiments, module 54 may include particular party-correlatedspeech interaction based adaptation data from particular partyassociated particular device receiving module 307.

Referring again to FIG. 3 (e.g., FIG. 3K), module 54 may includeparticular party-correlated speech interaction based adaptation dataselected based on previous speech interaction similarity with expectedfuture speech interaction particular device receiving module 309,particular party-correlated previous speech interaction based adaptationdata from particular-party speech detecting particular device receivingmodule 323, and particular party-correlated previous speech interactionbased adaptation data from particular-party speech recording particulardevice receiving module 325. In some embodiments, module 309 may includeparticular party-correlated speech interaction based adaptation dataselected based on use of specific vocabulary word particular devicereceiving module 311 and particular party-correlated previous speechinteraction based adaptation data from particular-party speech receivingparticular device receiving module 313. In some embodiments, module 313may include particular party-correlated previous speech interactionbased adaptation data from particular-party speech receiving smartphonereceiving module 315 and particular party-correlated previous speechinteraction based adaptation data from particular-party speech receivingparticular device having speech transmission software receiving module317. In some embodiments, module 317 may further include particularparty-correlated previous speech interaction based adaptation data fromparticular-party speech receiving tablet receiving module 319 andparticular party-correlated previous speech interaction based adaptationdata from particular-party speech receiving navigation device receivingmodule 321.

Referring now to FIG. 4, FIG. 4 illustrates an exemplary implementationof the received adaptation data to target device applying module 56. Asshown in FIG. 4 (e.g., FIG. 4A), received adaptation data to targetdevice applying module 56 may include received adaptation data to speechrecognition module of target device applying module 402, transmission ofreceived adaptation data to speech recognition module configured toprocess speech facilitating module 404, received adaptation data totarget device speech recognition module updating module 406, receivedadaptation data to target device speech recognition module modifyingmodule 408, received adaptation data to target device speech recognitionmodule adjusting module 410, received adaptation data includingpronunciation dictionary to target device speech recognition moduleapplying module 412, and received adaptation data including phonemedictionary to target device speech recognition module applying module414.

Referring again to FIG. 4 (e.g., FIG. 4B), module 56 may includereceived adaptation data including dictionary of target device relatedwords to target device speech recognition module applying module 416,received adaptation data including training set of audio data andcorresponding transcript data to target device applying module 418,received adaptation data including one or more word weightings data totarget device applying module 420, received adaptation data includingone or more words probability information to target device applyingmodule 422, received adaptation data processing for exterior speechrecognition module usage processing module 424, and accepted vocabularyof speech recognition module of target device modifying module 426.

Referring again to FIG. 4 (e.g., FIG. 4C), module 56 may includeaccepted vocabulary of speech recognition module of target devicereducing module 428 and accepted vocabulary of speech recognition moduleof target device removing module 430.

Referring now to FIG. 5, FIG. 5 illustrates an exemplary implementationof the target device particular party speech processing using receivedadaptation data module 58. For example, as shown in FIG. 5 (e.g., FIG.5A), target device particular party speech processing using receivedadaptation data module 58 may include at least one of speech and appliedadaptation data transmitting to interpreting device configured tointerpret at least a portion of speech module 502, speech recognitionmodule of target device particular party speech interpreting usingreceived adaptation data module 504, speech recognition module of targetdevice particular party speech converting into textual data usingreceived adaptation data module 506, and speech recognition module oftarget device particular party speech deciphering into word data usingreceived adaptation data module 508.

Referring again to FIG. 5 (e.g., FIG. 5B), module 58 may include speechanalysis based action carrying out by target device particular partyspeech processing using received adaptation data module 510 and motorvehicle particular party speech processing using received adaptationdata module 518. In some embodiments, module 510 may include speechanalysis based bank transaction carrying out by banking terminal targetdevice using received adaptation data module 512 and speech analysisbased bank transaction carrying out by banking terminal target deviceusing received adaptation data module 514 (e.g., which, in someembodiments, may include speech analysis based bank account moneywithdrawal by banking terminal target device using received adaptationdata module 516. In some embodiments, module 518 may include motorvehicle particular party speech processing into motor vehicle operationcommands using received adaptation data module 520, motor vehicleparticular party speech processing into motor vehicle particular systemoperation command using received adaptation data module 522 (e.g.,which, in some embodiments, may include motor vehicle particular partyspeech processing into one or more motor vehicle systems includingsound, navigation, information, and emergency response operationcommands using received adaptation data module 524), and motor vehicleparticular party speech processing into motor vehicle setting changecommand using received adaptation data module 526 (e.g., which, in someembodiments, may include motor vehicle particular party speechprocessing into motor vehicle seat position change command usingreceived adaptation data module 528.

Referring again to FIG. 5 (e.g., FIG. 5C), module 58 may include targetdevice setting based on recognition of particular party using speechrecognition module of target device applying using received adaptationdata module 530 and target device configuration changing based onrecognition of particular party using speech recognition module oftarget device module 532 (e.g., which, in some embodiments, may includedisc player subtitle language output changing based on recognition ofparticular party using speech recognition module of target device module534). In some embodiments, module 58 may include target device speechrecognition module particular party speech processing using receivedadaptation data module 536 (e.g., which, in some embodiments, mayinclude particular party processed speech confidence level determiningmodule 544 and adaptation data modifying based on determined confidencelevel of processed speech module 546), adaptation data modificationbased on processed speech from particular party deciding module 538,adaptation data modifying partly based on processed speech and partlybased on received information module 540, and modified adaptation datatransmitting to particular device module 542.

A more detailed discussion related to terminal device 30 of FIG. 1 nowwill be provided with respect to the processes and operations to bedescribed herein. Referring now to FIG. 6, FIG. 6 illustrates anoperational flow 600 representing example operations for, among othermethods, receiving indication of initiation of a speech-facilitatedtransaction between a particular party and a target device, receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party, applying the receivedadaptation data correlated to the particular party to the target device,and processing speech from the particular party using the target deviceto which the received adaptation data has been applied. In FIG. 6 and inthe following FIGS. 7-10 that include various examples of operationalflows, discussions and explanations will be provided with respect to theexemplary environment 100 as described above and as illustrated in FIG.1, and with respect to other examples (e.g., as provided in FIGS. 2-5)and contexts. It should be understood that the operational flows may beexecuted in a number of other environments and contexts, and/or inmodified versions of the systems shown in FIGS. 2-5. Although thevarious operational flows are presented in the sequence(s) illustrated,it should be understood that the various operations may be performed inother orders other than those which are illustrated, or may be performedconcurrently.

In some implementations described herein, logic and similarimplementations may include software or other control structures.Electronic circuitry, for example, may have one or more paths ofelectrical current constructed and arranged to implement variousfunctions as described herein. In some implementations, one or moremedia may be configured to bear a device-detectable implementation whensuch media hold or transmit device detectable instructions operable toperform as described herein. In some variants, for example,implementations may include an update or modification of existingsoftware or firmware, or of gate arrays or programmable hardware, suchas by performing a reception of or a transmission of one or moreinstructions in relation to one or more operations described herein.Alternatively or additionally, in some variants, an implementation mayinclude special-purpose hardware, software, firmware components, and/orgeneral-purpose components executing or otherwise invokingspecial-purpose components. Specifications or other implementations maybe transmitted by one or more instances of tangible transmission mediaas described herein, optionally by packet transmission or otherwise bypassing through distributed media at various times.

Following are a series of flowcharts depicting implementations. For easeof understanding, the flowcharts are organized such that the initialflowcharts present implementations via an example implementation andthereafter the following flowcharts present alternate implementationsand/or expansions of the initial flowchart(s) as either sub-componentoperations or additional component operations building on one or moreearlier-presented flowcharts. Those having skill in the art willappreciate that the style of presentation utilized herein (e.g.,beginning with a presentation of a flowchart(s) presenting an exampleimplementation and thereafter providing additions to and/or furtherdetails in subsequent flowcharts) generally allows for a rapid and easyunderstanding of the various process implementations. In addition, thoseskilled in the art will further appreciate that the style ofpresentation used herein also lends itself well to modular and/orobject-oriented program design paradigms.

Further, in FIG. 6 and in the figures to follow thereafter, variousoperations may be depicted in a box-within-a-box manner. Such depictionsmay indicate that an operation in an internal box may comprise anoptional example embodiment of the operational step illustrated in oneor more external boxes. However, it should be understood that internalbox operations may be viewed as independent operations separate from anyassociated external boxes and may be performed in any sequence withrespect to all other illustrated operations, or may be performedconcurrently. Still further, these operations illustrated in FIG. 6 aswell as the other operations to be described herein may be performed byat least one of a machine, an article of manufacture, or a compositionof matter.

It is noted that, for the examples set forth in this application, thetasks and subtasks are commonly represented by short strings of text.This representation is merely for ease of explanation and illustration,and should not be considered as defining the format of tasks andsubtasks. Rather, in various embodiments, the tasks and subtasks may bestored and represented in any data format or structure, includingnumbers, strings, Booleans, classes, methods, complex data structures,and the like.

Those having skill in the art will recognize that the state of the arthas progressed to the point where there is little distinction leftbetween hardware, software, and/or firmware implementations of aspectsof systems; the use of hardware, software, and/or firmware is generally(but not always, in that in certain contexts the choice between hardwareand software can become significant) a design choice representing costvs. efficiency tradeoffs. Those having skill in the art will appreciatethat there are various vehicles by which processes and/or systems and/orother technologies described herein can be effected (e.g., hardware,software, and/or firmware), and that the preferred vehicle will varywith the context in which the processes and/or systems and/or othertechnologies are deployed. For example, if an implementer determinesthat speed and accuracy are paramount, the implementer may opt for amainly hardware and/or firmware vehicle; alternatively, if flexibilityis paramount, the implementer may opt for a mainly softwareimplementation; or, yet again alternatively, the implementer may opt forsome combination of hardware, software, and/or firmware. Hence, thereare several possible vehicles by which the processes and/or devicesand/or other technologies described herein may be effected, none ofwhich is inherently superior to the other in that any vehicle to beutilized is a choice dependent upon the context in which the vehiclewill be deployed and the specific concerns (e.g., speed, flexibility, orpredictability) of the implementer, any of which may vary. Those skilledin the art will recognize that optical aspects of implementations willtypically employ optically-oriented hardware, software, and or firmware.

Throughout this application, examples and lists are given, withparentheses, the abbreviation “e.g.,” or both. Unless explicitlyotherwise stated, these examples and lists are merely exemplary and arenon-exhaustive. In most cases, it would be prohibitive to list everyexample and every combination. Thus, smaller, illustrative lists andexamples are used, with focus on imparting understanding of the claimterms rather than limiting the scope of such terms.

Portions of this application may reference trademarked companies andproducts merely for exemplary purposes. All trademarks remain the soleproperty of the trademark owner, and in each case where a trademarkedproduct or company is used, a similar product or company may bereplaced.

Referring again to FIG. 6, FIG. 6 shows operation 600 that includesoperation 602 depicting receiving indication of initiation of aspeech-facilitated transaction between a particular party and a targetdevice. For example, FIG. 1 shows speech-facilitated transactioninitiation between particular party and target device indicatorreceiving module 52 receiving indication (e.g., an electronic signalsent from an interface unit) of initiation (e.g., beginning, or about tobegin, e.g., a user walks up to a terminal, and may or may not beginspeaking) of a speech-facilitated transaction (e.g., an interactionbetween a user and a terminal, e.g., a bank terminal) in which at leastone component of the interaction uses speech (e.g., the user says “showme my balance” to the machine in order to display the balance on themachine) between a particular party (e.g., a user that wants to withdrawmoney from an ATM terminal) and a target device (e.g., an ATM terminal).

It is noted that the “indication” does not need to be an electronicsignal. The indication may come from a user interaction, from acondition being met, from the detection of a condition being met, orfrom a change in state of a sensor or device. The indication may be thatthe user has moved into a particular position, or has pushed a button,or is talking to the machine, or pressed a button on a portable device,or said a particular word or words, or made a gesture, or was capturedon a video camera. The indication may be an indication of an RFID tag

Referring again to FIG. 6, FIG. 6 shows operation 600 that also includesoperation 604 depicting receiving adaptation data correlated to theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 1 shows particular party-correlated previous speechinteraction based adaptation data from particular-party associatedparticular device receiving module 54 receiving (e.g., either locally orremotely) adaptation data (e.g., data related to speech processing, inthis case, a model for that user for words commonly used at an ATM like“withdraw” and “balance”) correlated to the particular party (e.g.,related to the way that the particular party speaks the words“withdraw,” “balance,” “one hundred,” and “twenty”), said receivingfacilitated (e.g., assisted in at least one step, e.g., sends theadaptation data or provides a location where the adaptation data may beretrieved) by a particular device (e.g., a smartphone) associated withthe particular party (e.g., carried by the particular party, or storesinformation regarding the particular party), wherein the adaptation datais at least partly based on previous adaptation data (e.g., adaptationdata from a prior interaction or conversation) derived at least in partfrom one or more previous speech interactions (e.g., the user takinginto a microphone at his computer).

Referring again to FIG. 6, FIG. 6 shows operation 600 that furtherincludes operation 606 depicting applying the received adaptation datacorrelated to the particular party to the target device. For example,FIG. 1 shows received adaptation data to target device speechrecognition module applying module 56 applying the received adaptationdata (e.g., the model for the particular user for commonly used ATMwords is applied to the ATM's default model for the commonly used ATMwords, replacing the default definitions with the user-specificdefinitions) correlated to the particular party (e.g., related to theway the particular party speaks) to the target device (the ATMterminal).

Referring again to FIG. 6, FIG. 6 shows operation 600 that still furtherincludes operation 608 depicting processing speech from the particularparty using the target device to which the received adaptation data hasbeen applied. For example, FIG. 1 shows target device speech recognitionmodule received speech processing module 58 processing speech (e.g., theverbal command “withdraw one hundred dollars” from the particular party(e.g., the user of the ATM) using the target device (e.g., the ATMTerminal) to which the received adaptation data (e.g., the user'sspecific model for commonly used ATM words) has been applied.

FIGS. 7A-7B depict various implementations of operation 602, accordingto embodiments. Referring now to FIG. 7A, operation 602 may includeoperation 702 depicting receiving indication of initiation of atransaction in which the particular party interacts with the targetdevice at least partly using speech. For example, FIG. 2 showsspeech-facilitated and partly using speech transaction initiationbetween particular party and target device indicator receiving module202 receiving indication (e.g., receiving a signal from a motion sensor)of initiation of a transaction (e.g., a user walks within a particularproximity of an airline ticket dispensing terminal) in which theparticular party (e.g., a user who wants to print out his airlineticket) interacts with the target device (e.g., the airline ticketdispensing terminal) at least partly using speech (e.g., the user sayswhich transaction he wants to perform, e.g., “print boarding pass,” butmay key in his flight number manually).

Referring again to FIG. 7A, operation 602 may include operation 704depicting receiving indication of initiation of a transaction in whichthe particular party interacts with the target device using only speech.For example, FIG. 2 shows speech-facilitated and only using speechtransaction initiation between particular party and target deviceindicator receiving module 204 receiving indication (e.g., receiving asignal from a credit card reader) of initiation of a transaction (e.g.,a user swipes a credit card in a public pay computer in a hotel) inwhich the particular party interacts with the target device (a publicpay computer) using only speech (e.g., there is no keyboard or mouse,just voice prompts).

Referring again to FIG. 7A, operation 602 may include operation 706depicting receiving indication of initiation of a transaction in whichthe particular party interacts with the target device at least partlyusing speech and partly interacting with one or more buttons of theterminal device. For example, FIG. 2 shows speech facilitatedtransaction using speech and terminal device button initiation indicatorreceiving module 206 receiving indication (e.g., receiving a signal thata user has powered on the locking interface mechanism of the safe,either by pressing a button or flipping a switch) of initiation of atransaction (e.g., a transaction to gain entry to a safe locked byelectronic means) in which the particular party (e.g., the persondesiring access to the safe) interacts with the target device (e.g., thesafe and the interface for unlocking it) at least partly using speech(e.g., speaking a command to the safe, or speaking a predefined phrasethat partially unlocks the safe) and partly interacting with one or morebuttons of the terminal device (e.g., a keypad on which the user entersa code in order to unlock the safe after speaking the predefinedphrase).

Referring again to FIG. 7A, operation 602 may include operation 707depicting receiving indication of initiation of a transaction in whichthe particular party interacts with the target device at least partlyusing speech and partly using one or more gestures. For example, FIG. 2shows speech facilitated transaction using speech and gesture initiationindicator receiving module 209 receiving indication (e.g., a signal thatan object has been placed on a particular surface) of initiation of atransaction (e.g., a user wants to purchase grocery items from aself-checkout) in which the particular party (e.g., the buyer ofgroceries) interacts with the target device (e.g., the self-checkoutstation) at least partly using speech (e.g., speaks “check out” to theterminal to indicate no more groceries) and partly using one or moregestures (e.g., hand movements or facial movements to indicate “yes” or“no”).

For another example, FIG. 2 shows speech facilitated transaction usingspeech and gesture initiation indicator receiving module 209 receivingindication (e.g., a login to a computer terminal in an enterprisebusiness setting) of initiation of a transaction (e.g., an employee ofthe company wants to use this particular terminal) in which theparticular party (e.g., a person who communicates through speech andgestures) interacts with the target device (e.g., a computer usable byall company employees with a valid login) at least partly using speech(e.g., speech-to-text inside a word processing document) and partlyusing one or more gestures (e.g., specific hand or facial gesturesdesigned to open and close various programs).

Referring again to FIG. 7A, operation 602 may include operation 708depicting receiving indication of initiation of a transaction in whichthe particular party interacts with the target device at least partlyusing speech and partly interacting with one or more screens of theterminal device. For example, FIG. 2 shows speech facilitatedtransaction using speech and terminal device screen initiation indicatorreceiving module 208 receiving indication of initiation of a transaction(e.g., detecting an RFID-equipped device located on the person of theuser) in which the particular party (e.g., the person who walks into acab) interacts with the target device (e.g., a device inside a taxi cabfor paying fares and entering the address) at least partly using speech(e.g., speaking the destination) and partly interacting with one or morescreens of the terminal device (e.g., using a touchscreen to confirm thecorrect location of the destination after it has been spoken by theparticular party).

Referring again to FIG. 7A, operation 602 may include operation 710depicting receiving indication of a property of a particular partyindicating intent to conduct a speech-facilitated transaction with thetarget device. For example, FIG. 2 shows particular party intention toconduct target device speech-facilitated transaction indicator receivingmodule 210 receiving indication of the particular party's (e.g., a user)one or more steps taken (e.g., holds an RFID identification card up toan electronic lock) to conduct a speech-facilitated transaction (e.g.,audible password verification) with the target device (e.g., a doorlock).

Referring again to FIG. 7A, operation 710 may include operation 712depicting receiving indication of an interaction between the particularparty and the target device. For example, FIG. 2 shows particular partyand target device interaction indication receiving module 712 receivingindication of an interaction (e.g., an opening of a program, or anactivation of a piece of hardware or software) between the particularparty (a computer user, in either a home or an enterprise setting) andthe target device (e.g., a desktop computer, or a laptop).

Referring again to FIG. 7A, operation 710 may include operation 714depicting receiving indication that the particular party is less than acalculated distance away from the target device. For example, FIG. 2shows particular party and target device particular proximity indicationreceiving module 214 receiving indication (e.g., a signal) that theparticular party (e.g., the user of a pharmacy terminal to check on aprescription) is less than a calculated distance away (e.g., less thanone (1) meter, indicating a desire to use that terminal) from the targetdevice (e.g., the pharmacy information terminal).

Referring now to FIG. 7B, operation 710 may include operation 716depicting receiving indication that the particular party is less than acalculated distance away from the target device, and receivingindication that a particular condition is met. For example, FIG. 2 showsparticular party and target device particular proximity and particularcondition indication receiving module 216 receiving indication that theparticular party (e.g., the user) is within a particular proximity(e.g., less than one meter away, and in the direction such that the usercan see the screen) of the target device (e.g., a hotel check-in systemthat has optional use of speech interaction or non-speech interaction),and receiving indication that a particular condition is met (e.g., it isan eligible time for hotel check-in).

Referring again to FIG. 7B, operation 716 may include operation 718depicting receiving indication that the particular party is less than acalculated distance away from the target device, and that the particularparty is carrying the particular device. For example, FIG. 2 showsparticular party and target device particular proximity and carryingparticular device indication receiving module 218 receiving indication(e.g., an electronic message from a device configured to detectindications) that the particular party (e.g., the user) is within aparticular proximity (e.g., within one (1) meter) of the target device(e.g., an airline ticket terminal), and that the particular party (e.g.,the user) is carrying the particular device (e.g., the user'ssmartphone, or the user's memory stick storing the adaptation data, orthe user's device that contains the address for retrieving theadaptation data).

Referring again to FIG. 7B, operation 602 may include operation 720depicting receiving indication that the particular party is speaking tothe target device. For example, FIG. 2 shows particular party speakingto target device indicator receiving module 220 receiving indication(e.g., receiving data from which it can be inferred) that the particularparty (e.g., the user) is speaking to the target device (e.g., thespeech-enabled television).

Referring again to FIG. 7B, operation 602 may include operation 722depicting receiving indication that the particular party is attemptingto speak to the target device. For example, FIG. 2 shows particularparty intending to speak to target device indicator receiving module 222receiving indication (e.g., receiving data indicating) that theparticular party (e.g., the user) is attempting to speak (e.g., istrying to speak but is not able, or has started to speak) to the targetdevice (e.g., the home security system control panel).

Referring again to FIG. 7B, operation 602 may include operation 724depicting receiving indication, from the particular device, ofinitiation of a speech-facilitated transaction between the particularparty and the target device. For example, FIG. 2 showsspeech-facilitated transaction initiation between particular party andtarget device indicator receiving from particular device module 224receiving indication (e.g., a signal or transmission of data), from theparticular device (e.g., the user's smartphone), of initiation of aspeech-facilitated transaction between the particular party (e.g., theuser and owner of the smartphone) and the target device (e.g., anautomated teller machine).

Referring again to FIG. 7B, operation 602 may include operation 726depicting receiving indication, from a further device, of initiation ofa speech-facilitated transaction between the particular party and thetarget device. For example, FIG. 2 shows speech-facilitated transactioninitiation between particular party and target device indicatorreceiving from further device module 226 receiving indication (e.g., atransmission of data), from a further device (e.g., a device that is notthe particular device, e.g., a microphone on a ticket processingterminal), of initiation of a speech-facilitated transaction (e.g.,buying a ticket to see a movie) between the particular party (e.g., theuser who desires to buy a movie ticket) and the target device (e.g., theticket processing terminal). It is noted that the further device may bethe target device, may be part of the target device, may be related tothe target device, or may be discrete from and/or unrelated to thetarget device.

Referring now to FIG. 7C, operation 602 may include operation 728depicting detecting initiation of a speech-facilitated transactionbetween a particular party and a target device. For example, FIG. 2shows speech-facilitated transaction initiation between particular partyand target device indicator detecting module 228 detecting initiation(e.g., determining a start) of a speech-facilitated transaction (e.g.,an arming or disarming of a door lock) between a particular party (e.g.,a homeowner) and a target device (e.g., a security system).

Referring again to FIG. 7C, operation 602 may include operation 730depicting detecting an execution of at least one machine instructionthat is configured to facilitate communication with the particular partythrough a speech-facilitated transaction. For example, FIG. 2 showsprogram configured to communicate with particular party throughspeech-facilitated transaction launch detecting module 230 detecting anexecution of at least one machine instruction (e.g., detecting carryingout of a program or a routine on a machine, e.g., on a user'ssmartphone) that is configured to facilitate communication (e.g., toreceive speech or portions of speech, or one or more voice models) withthe particular party (e.g., the user) through a speech-facilitatedtransaction (e.g., ordering food from an automated drive-thru window).

FIGS. 8A-8P depict various implementations of operation 604, accordingto embodiments. Referring now to FIG. 8A, operation 604 may includeoperation 802 depicting receiving adaptation data comprising speechcharacteristics of the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party. For example, FIG. 3 shows particularparty-correlated previous speech interaction based speechcharacteristics from particular-party associated particular devicereceiving module 302 receiving adaptation data (e.g., data formodifying, changing, creating, updating, replacing, or otherwiseinteracting with the portions of the target device dealing with speechprocessing) comprising speech characteristics of the particular party(e.g., speech patterns for particular words, syllable recognitioninformation, word recognition information, phoneme recognitioninformation, sentence recognition information, pronunciation recognitioninformation, and/or phrase recognition information), sad receivingfacilitated by (e.g. the adaptation data is transmitted by) a particulardevice (e.g., a user's smartphone) associated with the particular party(e.g., in the particular party's possession), wherein the adaptationdata is at least partly based on previous adaptation data (e.g.,adaptation data that existed previously to the adaptation data that istransferred) derived at least in part from one or more previous speechinteractions (e.g., speech interactions between a user and anotherperson, or speech interactions between a user and another terminal) ofthe particular party (e.g., the user).

Referring again to FIG. 8A, operation 604 may include operation 804depicting receiving adaptation data comprising instructions for adaptingone or more speech recognition modules from a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 3 shows particular party-correlated previous speechinteraction based instructions for adapting one or more speechrecognition modules from particular-party associated particular devicereceiving module 304 receiving adaptation data comprising instructionsfor adapting (e.g., instructions for modifying the speech recognitionmodule in order to more efficiently process speech from the particularparty) one or more speech recognition modules (e.g., hardware orsoftware in the target device or an intermediary device) from aparticular device (e.g., a device carried by the user that stores and/ortransmits adaptation data) associated with the particular party (e.g.,owned by the particular party), wherein the adaptation data is at leastpartly based on previous adaptation data (e.g., different adaption data)derived at least in part from one or more previous speech interactions(e.g., a user talking to his computer equipped with a microphone) of theparticular party.

Referring again to FIG. 8A, operation 604 may include operation 806depicting receiving adaptation data comprising instructions for updatingone or more speech recognition modules from a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 3 shows particular party-correlated previous speechinteraction based instructions for updating one or more speechrecognition modules from particular-party associated particular devicereceiving module 306 receiving adaptation data comprising instructionsfor updating (e.g., adding, replacing, modifying, or otherwise changinga module, or in the absence of an existing module, creating one) one ormore speech recognition modules (e.g., hardware or software in thetarget device or an intermediary device configured to facilitate speech)from a particular device (e.g., a specialized adaptation data storageand transmitting device carried by the user, e.g., on a keychain)associated with the particular party (e.g., bought or registered by theparticular party), wherein the adaptation data is at least partly basedon previous adaptation data (e.g., different adaptation data) derived atleast in part from one or more previous speech interactions (e.g., auser commanding a Blu-ray player to fast-forward, pause, stop, and playBlu-ray discs.

Referring again to FIG. 8A, operation 604 may include operation 808depicting receiving adaptation data comprising instructions formodifying one or more speech recognition modules from a particulardevice associated with the particular party, wherein the adaptation datais at least partly based on previous adaptation data derived at least inpart from one or more previous speech interactions of the particularparty. For example, FIG. 3 shows particular party-correlated previousspeech interaction based instructions for modifying one or more speechrecognition modules from particular-party associated particular devicereceiving module 308 receiving adaptation data comprising instructionsfor modifying (e.g., changing in some way in order to potentiallyimprove at least one aspect of) one or more speech recognition modules(e.g., hardware or software that is discrete and capable ofindependently operating and interfacing with the target device) from aparticular device (e.g., a device designed to facilitate different typesof access for disabled people, e.g., a specialized wheelchair), whereinthe adaptation data is at least partly based on previous adaptation data(e.g., pronunciation keys for the particular party saying commonly-usedwords) derived at least in part from one or more previous speechinteractions of the particular party (e.g., previous speech interactionswith terminals of similar types, e.g., airline ticket dispensingterminals).

Referring again to FIG. 8A, operation 604 may include operation 810depicting receiving adaptation data comprising data linkingpronunciation of one or more phonemes by the particular party to one ormore concepts, from a particular device associated with the particularparty, wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party. For example, FIG. 3 showsparticular party-correlated previous speech interaction based datalinking particular party pronunciation of one or more words to one ormore words from particular-party associated particular device receivingmodule 310 receiving adaptation data comprising data linkingpronunciation of one or more phonemes (e.g., “/h/”/bcj/”) by theparticular party (e.g., the person involved in the speech-facilitatedtransaction) to one or more concepts (e.g., the phoneme “/s/” is linkedto the letter “−s” appended at the end of a word), from a particulardevice (e.g., an interface tablet carried by the user) associated withthe particular party (e.g., the particular party is logged in as a userof the particular device), wherein the adaptation data is at leastpartly based on previous adaptation data (e.g., adaptation data of asame type, e.g., phonemes linked to concepts) derived at least in partfrom one or more previous speech interactions (e.g., the user trainingthe interface tablet to respond to particular voice commands) of theparticular party.

Referring now to FIG. 8B, operation 604 may include operation 812depicting receiving data comprising a location at which adaptation datacorrelated to the particular party is available, from a particulardevice associated with the particular party, wherein the adaptation datais at least partly based on previous adaptation data derived at least inpart from one or more previous speech interactions of the particularparty. For example, FIG. 3 shows particular party-correlated previousspeech interaction based data locating available particular partycorrelated adaptation data from particular-party associated particulardevice receiving module 312 receiving data comprising a location (e.g.,a web address or server location address expressed as an IPv4 or IPv6address) at which adaptation data (e.g., pronunciation models of the tenwords most commonly used to interact with the target device) correlatedto the particular party is available (e.g., able to be retrieved, eitherprotected by a password, encryption, or otherwise unprotected), from aparticular device (e.g., a small token that stores a location and anauthentication password for accessing the data at the location)associated with the particular party (e.g., carried by the particularparty, or stored inside an object on the particular party, e.g., insidea pair of eyeglasses), wherein the adaptation data is at least partlybased on previous adaptation data (e.g., slightly differentpronunciation models of the words most commonly used to interact withthe target device, or a different set of words for interacting with adifferent target device) derived at least in part from one or moreprevious speech interactions (e.g., previous speech interactions with amotor vehicle) of the particular party.

Referring again to FIG. 8B, operation 604 may include operation 895depicting receiving adaptation data comprising data linkingpronunciation of one or more audibly distinguishable sounds by theparticular party to one or more concepts, from a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 3 shows particular party-correlated audiblydistinguishable sound linking to concept adaptation data fromparticular-party associated particular device receiving module 395receiving adaptation data comprising data linking pronunciation (e.g.,the way the user pronounces) of one or more audibly distinguishablesounds (e.g., phonemes or morphemes) by the particular party (e.g., theuser, having logged into his work computer, attempting to train the workcomputer to the user's voice) to one or more concepts (e.g.,combinations of phonemes and morphemes into words such as “openMicrosoft Word,” which opens the word processor for the user), from aparticular device associated with the particular party (e.g., a USB“thumb” drive that is inserted into the work computer, such that the USBdrive may or may not also include the user's credentials, verification,or login information), wherein the adaptation data is at least partlybased on previous adaptation data (e.g., adaptation data derived from aprevious training of a different computer) derived at least in part fromone or more previous speech interactions of the particular party (e.g.,the user previously trained on a different computer, which may or maynot have been part of the enterprise solution, e.g., the computer couldhave been a home computer, or a computer from a different company, orfrom a different division of the same company).

Referring again to FIG. 8B, operation 604 may include operation 814depicting receiving data comprising authorization to receive adaptationdata correlated to the particular party, from a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 3 shows particular party-correlated previous speechinteraction based authorization to receive data correlated to particularparty from particular-party associated particular device receivingmodule 314 receiving data comprising authorization (e.g., a code,password, key, security level setting, or other feature designed toprovide access) to receive adaptation data (e.g., example accuracy ratesof various speech models previously used, so that a system can pick onethat it desires based on accuracy rates and projected type of usage)correlated to the particular party (e.g., the accuracy rates are, atleast in part, based on previous interactions by the particular party),from a particular device associated with the particular party (e.g.,transmitted from a cellular or wireless radio communication devicecarried by the particular party), wherein the adaptation data is atleast partly based on previous adaptation data (e.g., other accuracyrates of various speech models that are updated after speech-facilitatedinteractions by the particular party) derived at least in part from oneor more previous speech interactions of the particular party (e.g., eachtime a speech-facilitated interaction by the particular party isfacilitated by the particular device, adaptation data is stored, andupdated if warranted).

Referring again to FIG. 8B, operation 604 may include operation 816depicting receiving data comprising instructions for obtainingadaptation data correlated to the particular party, from a particulardevice associated with the particular party, wherein the adaptation datais at least partly based on previous adaptation data derived at least inpart from one or more previous speech interactions of the particularparty. For example, FIG. 3 shows particular party-correlated previousspeech interaction based instructions for obtaining adaptation data fromparticular-party associated particular device receiving module 316receiving data comprising instructions for obtaining adaptation data(e.g., data including one or more of locations, login information,credential information, screens for displaying, software needed toobtain adaptation data, a list of hardware compatible with theadaptation data, etc.) correlated to the particular party (e.g., theinstructions are for locating the adaptation data related to theparticular party), from a particular device (e.g., a smartphone)associated with the particular party (e.g., the user has a servicecontract for the smartphone), wherein the adaptation data (e.g., speechmodel adaptation instructions) is at least partly based on previousadaptation data (e.g., less-recently updated speech model adaptationinstructions) derived at least in part (e.g., the speech modeladaptation information is updated based upon the success of the one ormore previous speech interactions) from one or more previous speechinteractions (e.g., interactions with speech facilitated systems, e.g.,bank or credit card systems that use an automated answering and routingsystem) of the particular party.

Referring again to FIG. 8B, operation 604 may include operation 818depicting receiving adaptation data including particular partyidentification data and data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data including particular party identification data fromparticular-party associated particular device receiving module 318receiving adaptation data (e.g., a word acceptance algorithm tailored tothe particular party, e.g., the user) including particular partyidentification data (e.g., data identifying the particular party, eitherin a specific (e.g., “John Smith”) or a non-specific (e.g., “Bank ofAmerica account holder”) manner and data correlated to the particularparty (e.g., the aforementioned word acceptance algorithm), saidreceiving facilitated by a particular device (e.g., a smartphone thatprovides the location where the word acceptance algorithm may beretrieved, e.g., a website, e.g.,“https://www.fakeurl.com/acceptancealgorithm0101011.html”) associatedwith the particular party (e.g., the user is carrying the smartphone),wherein the adaptation data is at least partly based on previousadaptation data (e.g., an earlier version of the word acceptancealgorithm) derived at least in part from one or more previous speechinteractions (e.g., a user's speech interaction with an automated phoneanswering and routing system) of the particular party.

Referring again to FIG. 8B, operation 818 may include operation 820depicting receiving adaptation data uniquely identifying the particularparty and correlated to the particular party, said receiving facilitatedby a particular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party. For example, FIG. 3 shows particularparty-correlated previous speech interaction based adaptation dataincluding particular party unique identification data fromparticular-party associated particular device receiving module 320receiving adaptation data (e.g., a probabilistic word model based onthat particular user and the target device to which the user isinteracting, which is a subset of the total adaptation data facilitatedby the particular device, which may include a library of probabilisticword models for different target devices, e.g., different models for anATM machine and a DVD player) uniquely identifying the particular party(e.g., the probabilistic word model of John Smith, or the probabilisticword model of a user having the username SpaceBot_(—)0901)) andcorrelated to the particular party, said receiving facilitated by aparticular device (e.g., a headset and microphone which also is capableof storing and/or transmitting and receiving data) associated with theparticular party (e.g., being worn by the user), wherein the adaptationdata (e.g., the probabilistic word model) is at least partly based onprevious adaptation data (e.g., a prior probabilistic word model that isupdated at periodic intervals) derived at least in part from one or moreprevious speech interactions (e.g., speech interactions using theparticular device, e.g., the headset and microphone) of the particularparty (e.g., the user wearing the headset and microphone).

Referring now to FIG. 8C, operation 604 may include operation 822depicting receiving adaptation data correlated to the particular partyfrom a particular device owned by the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party. For example, FIG. 3 shows particularparty-correlated previous speech interaction based adaptation data fromparticular-party owned particular device receiving module 322 receivingadaptation data (e.g., an expected response-based algorithm) correlatedto the particular party (e.g., tailored to one or more of the particularparty's speech characteristics and expected responses) from a particulardevice (e.g., a key for a motor vehicle that stores adaptation data)owned by the particular party (e.g., the owner of the motor vehicle ownsthe key), wherein the adaptation data is at least partly based onprevious adaptation data (e.g., a prior expected response-basedalgorithm) derived at least in part from one or more previous speechinteractions (e.g., previous times the driver has used the key to startthe motor vehicle and interacted with the motor vehicle using speech) ofthe particular party (e.g., the user).

Referring again to FIG. 8C, operation 604 may include operation 824depicting receiving adaptation data correlated to the particular partyfrom a particular device carried by the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party. For example, FIG. 3 shows particularparty-correlated previous speech interaction based adaptation data fromparticular-party carried particular device receiving module 324receiving adaptation data (e.g., a best-model selection algorithm)correlated to the particular party (e.g., at least a portion of thealgorithm is related to the user in some manner), from a particulardevice carried by the particular party (e.g., an identification badgeconfigured to store and transmit data), wherein the adaptation data isat least partly based on previous adaptation data (e.g., a priorbest-model selection algorithm, which may have had fewer models,different models, or a different manner of selecting models) derived atleast in part from one or more previous speech interactions (e.g., eachinteraction with a different type of device creates a new model andchanges the selection process of the model) of the particular party(e.g., the user).

Referring again to FIG. 8C, operation 604 may include operation 826depicting receiving adaptation data correlated to the particular partyfrom a particular device previously used by the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party. For example, FIG. 3 showsparticular party-correlated previous speech interaction based adaptationdata from particular device previously used by particular partyreceiving module 326 receiving adaptation data (e.g., a word conversionhypothesizer) correlated to the particular party (e.g., the user, andthe word conversion hypothesizer has at least one feature that is basedon at least one property of the user's speech) from a particular device(e.g., a user's smartphone) previously used by the particular party(e.g., the user has previously operated the cellphone, such operationbeing any function, regardless of whether it is speech-facilitated),wherein the adaptation data is at least partly based on previousadaptation data (e.g., an earlier word conversion hypothesizer, whichmay be the same word conversion hypothesizer, if no modifications havebeen made) derived at least in part from one or more previous speechinteractions of the particular party (e.g., a base word conversionhypothesizer was loaded on the particular device, and after each speechinteraction by the particular party, a decision is made regardingwhether to update or modify the word conversion hypothesizer, based on aresult or a perceived result of the speech interaction with theparticular party).

Referring again to FIG. 8C, operation 604 may include operation 828depicting receiving adaptation data correlated to the particular partyfrom a particular device for which a service contract is affiliated withthe particular party, wherein the adaptation data is at least partlybased on previous adaptation data derived at least in part from one ormore previous speech interactions of the particular party. For example,FIG. 3 shows particular party-correlated previous speech interactionbased adaptation data from particular-party service contract affiliatedparticular device receiving module 328 receiving adaptation data (e.g.,a continuous word recognition module) correlated to the particular party(e.g., the continuous word recognition module has been tailored to theparticular party based on speech patterns of the particular party) froma particular device (e.g., a cellular telephone) for which a servicecontract (e.g., a two-year contract for cellular service with AT&T) isaffiliated with the particular party (e.g., it is the user that signedthe contract for cellular service with AT&T that covers the cellulartelephone), wherein the adaptation data (e.g., the continuous wordrecognition module) is least partly based on previous adaptation data(e.g., an incomplete continuous word recognition module that waspreviously not used, but after a number of speech interactions, hadenough data for a complete continuous word recognition module that isused to assist in speech-facilitated transactions) derived at least inpart from one or more previous speech interactions (e.g., interactionswith devices that use a combination of hardware or software to recognizespeech) of the particular party.

Referring again to FIG. 8C, operation 604 may include operation 830depicting receiving adaptation data correlated to the particular partyfrom a particular device of which the particular party is a user,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party. For example, FIG. 3 showsparticular party-correlated previous speech interaction based adaptationdata from particular device used by particular party receiving module330 receiving adaptation data (e.g., tailored utterance recognitioninformation) correlated to the particular party (e.g., the utterancerecognition information is tailored to the particular party, e.g., theuser) from a particular device (e.g., a laptop computer) of which theparticular party is a user (e.g., the particular party has at least onceused the laptop computer, or the laptop computer is configured torecognize the particular party as a person who has access to use thelaptop computer), wherein the adaptation data (e.g., the tailoredutterance recognition information) is at least partly based on previousadaptation data (e.g., prior tailored utterance recognition information,which may be compiled from the particular party as well as other users,e.g., other users of the laptop computer, or other users generally)derived at least in part from one or more previous speech interactionsof the particular party (e.g., the particular party, as well as otherparties, may communicate with the laptop computer through a speechinteraction).

Referring now to FIG. 8D, operation 604 may include operation 832depicting receiving adaptation data correlated to the particular partyfrom a particular device configured to allow the particular party to login, wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party. For example, FIG. 3 showsparticular party-correlated previous speech interaction based adaptationdata particular device configured to allow particular party loginreceiving module 332 receiving adaptation data (e.g., adaptable wordtemplates) correlated to the particular party (e.g., a user) from aparticular device configured to allow the particular party to log in(e.g., a generic speech facilitation unit that is reusable, e.g., may bedistributed or handed out, e.g., inside a museum, or in an airplane, andthat allows user login, and once a user logs in, retrieves theadaptation data, e.g., the adaptable word templates for that user, froma central repository), wherein the adaptation data (e.g., the adaptableword templates) is at least partly based on previous adaptation data(e.g., the selection of an adaptable word template is based on previousselections of an adaptable word template and the perceived result, e.g.,the system may know that adaptable word template A2 was used twice, andadaptable word template A4 was used three times, and adaptable wordtemplate B4 was used eight times, and other adaptable word templates C2,B6, A3, and A7 were each used once, then an adaptable word template withcharacteristics of B4 and characteristics specific to the expectedspeech interaction may be chosen as the adaptation data, e.g., adaptableword template C4) derived at least in part (e.g., the selection of theadaptable word template is at least partially controlled by previousselections of adaptable word templates) from one or more previous speechinteractions (e.g., at least one previous speech interaction for whichan adaptable word template was selected) of the particular party.

Referring now to FIG. 8D, operation 604 may include operation 834depicting receiving adaptation data correlated to the particular partyfrom a particular device configured to store data regarding theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data particular device configured to store particular partydata receiving module 334 receiving adaptation data (e.g., a speechprocessing algorithm specification) correlated to the particular party(e.g., at least a portion of the speech processing algorithmspecification is related to the user) from a particular device (e.g., asmartphone) configured to store data regarding the particular party(e.g., demographic data, identification data, or any other type of dataabout the user), wherein the adaptation data (e.g., the speechprocessing algorithm specification) is at least partly based on previousadaptation data (e.g., an older version of the speech processingalgorithm specification) derived at least in part from one or moreprevious speech interactions of the particular party (e.g., the olderversion of the speech processing algorithm specification is based onprevious speech interactions the user as with various machinesconfigured to receive speech as input).

Referring again to FIG. 8D, operation 834 may include operation 836depicting receiving adaptation data correlated to the particular partyfrom a particular device configured to store profile data regarding theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data particular device configured to store particular partyprofile data receiving module 336 receiving adaptation data (e.g.,algorithm selection data) correlated to the particular party (e.g., thealgorithm selection data is based on selecting the best algorithm forthe particular user involved in the speech-facilitated transaction) froma particular device (e.g., a server stored remotely from the user)configured to store profile data (e.g., data about the user) regardingthe particular party (e.g., the user), wherein the adaptation data(e.g., algorithm selection data) is at least partly based on previousadaptation data (e.g., previous versions of the algorithm selectiondata, which may be the same as the algorithm selection data) derived atleast in part (e.g., the algorithm selection data may be based on manyfactors, of which the speech characteristics of the user may be one)from one or more previous speech interactions of the particular party(e.g., a particular algorithm is selected based on the algorithmselection data from a previous speech interaction, and a perceivedsuccess of the previous speech interaction is determined, and theselected particular algorithm is stored along with its success rate, aswell as various other characteristics of the speech interaction, e.g.,which words were used, and what type of machine the user interactedwith).

Referring again to FIG. 8D, operation 834 may include operation 838depicting receiving adaptation data correlated to the particular partyfrom a particular device configured to store data unrelated to speechrecognition modules regarding the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party. For example, FIG. 3 shows particularparty-correlated previous speech interaction based adaptation dataparticular device configured to store particular party speech profileunrelated data receiving module 338 receiving adaptation data (e.g.,phoneme mapping algorithm) correlated to the particular party (e.g., theuser) from a particular device (e.g., a digital music player) configuredto store data (e.g., music preference information, e.g., or informationregarding a social network profile, e.g., a Facebook or Twitter profile)unrelated to speech recognition modules regarding the particular party(e.g., the user), wherein the adaptation data (e.g., the phoneme mappingalgorithm) is at least partly based on previous adaptation data (e.g., aprevious phoneme mapping algorithm that is different only in itsprocessing of the “w” sound phoneme) derived at least in part from oneor more previous speech interactions of the particular party (e.g., thephoneme mapping algorithm is modifiable by speech interactions that theuser undertakes.

Referring again to FIG. 8D, operation 604 may include operation 840depicting receiving adaptation data correlated to the particular partyfrom a particular device located within a particular proximity to theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data from particular device in particular proximity toparticular party receiving module 340 receiving adaptation data (e.g.,instructions for modifying a vocable recognition system) correlated tothe particular party (e.g., the user, and the instructions for modifyingare correlated to the user) from a particular device (e.g., from anobject on a keychain) located within a particular proximity to theparticular party (e.g., the particular party is located within a sphere1 m in diameter around the object on the keychain), wherein theadaptation data (e.g., instructions for modifying a vocable recognitionsystem) is at least partly based on previous adaptation data (e.g.,prior instructions for modifying a vocable recognition system) derivedat least in part from one or more previous speech interactions of theparticular party (e.g., the prior instructions are at least partly basedon observed outcomes of previous speech interactions).

Referring now to FIG. 8E, operation 604 may include operation 842depicting receiving adaptation data correlated to the particular partyfrom a particular device positioned closer to the particular party thanother devices, wherein the adaptation data is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data from particular-party associated particular devicecloser to particular party receiving module 342 receiving adaptationdata (e.g., a speech disfluency recognition algorithm) correlated to theparticular party (e.g., the algorithm is tailored to recognize speechdisfluencies of the particular party, e.g., the user) from a particulardevice (e.g., a smartphone) positioned closer to the particular party(e.g., the user) than other devices (e.g., other smartphones carried byother people, e.g., in order to distinguish, in a group, between theparticular party's smartphone and other smartphones which may or may notbe proffering adaptation data), wherein the adaptation data (e.g., thespeech disfluency recognition algorithm) is at least partly based onprevious adaptation data (e.g., an outdated or previously used speechdisfluency recognition algorithm) derived at least in part from one ormore previous speech interactions of the particular party (e.g., storedprevious speech interactions of the particular party are retrieved fromlocations at which such interactions are stored, and the speech isanalyzed for speech disfluencies, which are then identified andcategorized so that they may be recognized in future speechinteractions).

Referring again to FIG. 8E, operation 604 may include operation 844depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with one or moredevices other than the target device. For example, FIG. 3 showsparticular party-correlated previous other device speech interactionbased adaptation data from particular-party associated particular devicereceiving module 344 receiving adaptation data (e.g., a speechdisfluency deletion algorithm) correlated to the particular party (e.g.,the user), said receiving facilitated by a particular device (e.g., theparticular device, e.g., a tablet or smartphone, may provide an addressor instructions for receiving the adaptation data) associated with theparticular party (e.g., the user), wherein the adaptation data (e.g.,the speech disfluency deletion algorithm) is at least partly based onprevious adaptation data (e.g., older speech disfluency deletionalgorithms) derived at least in part from one or more previous speechinteractions (e.g., speech-facilitated transactions between the user anda device configured to accept speech as input) of the particular party(e.g., the user) with one or more devices (e.g., a big screen televisionthat accepts speech input) other than the target device (e.g., aspeech-enabled DVD player).

Referring again to FIG. 8E, operation 604 may include operation 846depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with one or moredevices related to the target device. For example, FIG. 3 showsparticular party-correlated previous other related device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 346 receiving adaptation data (e.g.,a discourse marker ignoring algorithm) correlated to the particularparty (e.g., the user), said receiving facilitated by a particulardevice (e.g., a universal remote control) associated with the particularparty (e.g., the user owns the universal remote control), wherein theadaptation data (e.g., the discourse marker ignoring algorithm) is atleast partly based on previous adaptation data (e.g., previous discoursemarker ignoring algorithms) derived at least in part from one or moreprevious speech interactions (e.g., setting the volume) of theparticular party (e.g., the user), with one or more devices (e.g., anaudio visual receiver) related to the target device (e.g., a Blu-Rayplayer, related to the A/V receiver in that they are both components ofcommon home theater systems).

Referring again to FIG. 8E, operation 604 may include operation 848depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with devices usingan intersecting vocabulary as the target device. For example, FIG. 3shows particular party-correlated previous other device having samevocabulary as target device speech interaction based adaptation datafrom particular-party associated particular device receiving module 348receiving adaptation data (e.g., non-purposeful filler filter algorithm)correlated to the particular party (e.g., the user), said receivingfacilitated by a particular device (e.g., a networked home computer)associated with the particular party (e.g., owned, set up, or used bythe user), wherein the adaptation data (e.g., the non-purposeful fillerfilter algorithm) is at least partly based on previous adaptation data(e.g., a previous non-purposeful filler filter algorithm) derived atleast in part from one or more previous speech interactions of theparticular party (e.g., voice commands from the user) with devices(e.g., media players) using an intersecting vocabulary (e.g., having atleast one word the same, e.g., “power off”) as the target device (e.g.,video game systems).

Referring again to FIG. 8E, operation 604 may include operation 850depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions with one or more devices manufactured bythe same manufacturer as the target device. For example, FIG. 3 showsparticular party-correlated previous other device having samemanufacturer as target device speech interaction based adaptation datafrom particular-party associated particular device receiving module 350receiving adaptation data (e.g., particularized vocabulary adjuster)correlated to the particular party (e.g., the user), said receivingfacilitated by a particular device (e.g., an adaptation data storagedevice carried by users and configured to store, transmit, and receiveadaptation data) associated with the particular party (e.g., stores datacorrelated to the user), wherein the adaptation data is at least partlybased on previous adaptation data (e.g., a previous particularizedvocabulary adjuster) derived at least in part from one or more previousspeech interactions with one or more devices (e.g., Apple iPhone)manufactured by the same manufacturer (e.g., Apple) as the target device(e.g., Apple TV).

Referring now to FIG. 8F, operation 604 may include operation 852depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions with one or more devices configured tocarry out similar functions as the target device. For example, FIG. 3shows particular party-correlated previous other similar-functionconfigured device speech interaction based adaptation data fromparticular-party associated particular device receiving module 352receiving adaptation data (e.g., vocabulary word weighting modificationalgorithm) correlated to the particular party (e.g., a user), saidreceiving facilitated by a particular device (e.g., a speechfacilitating tool) associated with the particular party (e.g., kept inthe particular party's house), wherein the adaptation data is at leastpartly based on previous adaptation data (e.g., a previous vocabularyword weighting modification algorithm) derived at least in part from oneor more previous speech interactions (e.g., operating a device at leastpartially through speech) with one or more devices (e.g., a stereosystem and a radio) configured to carry out similar functions (e.g.,playing sound, having a volume control) as the target device (e.g., aspeech input enabled television).

Referring again to FIG. 8F, operation 604 may include operation 854depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions with one or more devices configured tocarry out one or more same functions as the target device. For example,FIG. 3 shows particular party-correlated previous other same-functionconfigured device speech interaction based adaptation data fromparticular-party associated particular device receiving module 354receiving adaptation data (e.g., a speech deviation algorithm, e.g.,based on the user's speech patterns under particular conditions, e.g.,stress) correlated to the particular party (e.g., the user), saidreceiving facilitated by a particular device (e.g., a home monitoringsystem) associated with the particular party (e.g., installed in theuser's home), wherein the adaptation data (e.g., the speech deviationalgorithm) is at least partly based on previous adaptation data (e.g., aprevious speech deviation algorithm) derived at least in part from oneor more previous speech interactions with one or more devices (e.g., adoor lock system) configured to carry out one or more same functions(e.g., locking) as the target device (e.g., a safe, or an interior dooror window locking system).

Referring again to FIG. 8F, operation 604 may include operation 856depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions with one or more devices that previouslycarried out a same function as the target device is configured to carryout. For example, FIG. 3 shows particular party-correlated other devicespreviously carrying out same function as target device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 356 receiving adaptation data (e.g.,non-lexical vocable discarding algorithm) correlated to the particularparty (e.g., the user), said receiving facilitated by a particulardevice (e.g., a smartphone) associated with the particular party (e.g.,the user), wherein the adaptation data is at least partly based onprevious adaptation data (e.g., a previous non-lexical vocablediscarding algorithm) derived at least in part from one or more previousspeech interactions (e.g., programming the previous DVD player) with oneor more devices (e.g., old, possibly now-discarded DVD players) thatpreviously carried out a same function (e.g., playing DVDs) as thetarget device (e.g., a new DVD player) is configured to carry out.

Referring again to FIG. 8F, operation 604 may include operation 858depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or morespeech interactions with at least one device of a same type as thetarget device. For example, FIG. 3 shows particular party-correlatedprevious other same-type device speech interaction based adaptation datafrom particular-party associated particular device receiving module 358receiving adaptation data (e.g., instructions for an adaptation controlmodule) correlated to the particular party (e.g., tailored to the user),said receiving facilitated by a particular device (e.g., a hand-heldPDA) associated with the particular party (e.g., owned by the user),wherein the adaptation data (e.g., the instructions for an adaptationcontrol module) is at least partly based on previous adaptation data(e.g., a previous instruction for an adaptation control module) derivedat least in part from one or more speech interactions with at least onedevice (e.g., a netbook) of a same type (e.g., a computer) as the targetdevice (e.g., a desktop computer).

Referring again to FIG. 8F, operation 604 may include operation 860depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or morespeech interactions with the particular device. For example, FIG. 3shows particular party-correlated previous particular device speechinteraction based adaptation data from particular-party associatedparticular device receiving module 360 receiving adaptation data (e.g.,a phoneme pronunciation guide) correlated to the particular party (e.g.,the user), said receiving facilitated by a particular device (e.g., asmartphone) associated with the particular party (e.g., owned oroperated by the user), wherein the adaptation data (e.g., the phonemepronunciation guide) is at least partly based on previous adaptationdata (e.g., an earlier version, which may be identical, of the phonemepronunciation guide) derived at least in part from one or more speechinteractions (e.g., programming the device, or making a call on thedevice) with the particular device (e.g., the smartphone).

Referring now to FIG. 8G, operation 604 may include operation 862depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions observed by the particular device. Forexample, FIG. 3 shows particular party-correlated previous speechinteractions observed by particular device based adaptation data fromparticular-party associated particular device receiving module 362receiving adaptation data (e.g., a syllable pronunciation guide)correlated to the particular party (e.g., the user), said receivingfacilitated by a particular device (e.g., a smartphone) associated withthe particular party (e.g., carried by the particular party), whereinthe adaptation data (e.g., the syllable pronunciation guide) is at leastpartly based on previous adaptation data (e.g., a previous syllablepronunciation guide) derived at least in part from one or more previousspeech interactions (e.g., the user interacting with a terminal thataccepts speech) observed (e.g., recorded by the smartphone's microphone)by the particular device (e.g., the smartphone).

Referring again to FIG. 8G, operation 604 may include operation 864depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party, wherein saidadaptation data is correlated to one or more vocabulary words. Forexample, FIG. 3 shows particular party-correlated previous speechinteraction based adaptation data correlated to one or more vocabularywords and received from particular-party associated particular devicereceiving module 364 receiving adaptation data (e.g., a wordpronunciation guide) correlated to the particular party (e.g., a guideof how the user pronounces words), said receiving facilitated by aparticular device (e.g., a portable tablet computer) associated with theparticular party (e.g., operated by the user), wherein the adaptationdata (e.g., the word pronunciation guide) is at least partly based onprevious adaptation data (e.g., a previous word pronunciation guide,which may be the same, or may have different or fewer words, or may havedifferent or more pronunciations, or different favorite pronunciationsof words) derived at least in part from one or more previous speechinteractions of the particular party (e.g., speech-facilitatedtransactions between the user and at least one device configured toreceive speech input), wherein said adaptation data is correlated to oneor more vocabulary words (e.g., the adaptation data deals with one ormore vocabulary words).

Referring again to FIG. 8G, operation 864 may include operation 866depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party, wherein saidadaptation data is correlated to one or more vocabulary words used bythe target device. For example, FIG. 3 shows particular party-correlatedprevious speech interaction based adaptation data correlated to one ormore target device vocabulary words and received from particular-partyassociated particular device receiving module 366 receiving adaptationdata (e.g., a subset of a word pronunciation guide) correlated to theparticular party (e.g., a guide of the pronunciation keys for at leastone word), said receiving facilitated by a particular device (e.g., apocket electronic dictionary device, or a pocket translator device)associated with the particular party (e.g., owned by the user), whereinthe adaptation data is correlated to one or more vocabulary words usedby the target device (e.g., the one or more vocabulary words used by thetarget device, e.g., an Automated Teller Machine, may be “deposit,” andthe one or more vocabulary words used by the target device may beincluded in, but not necessarily exclusively, the subset of the wordpronunciation guide).

Referring again to FIG. 8G, operation 604 may include operation 868depicting requesting adaptation data correlated to the particular partyfrom the particular device associated with the particular party. Forexample, FIG. 3 shows particular party-correlated adaptation data fromparticular party associated particular device requesting module 368requesting adaptation data (e.g., a phoneme pronunciation guide)correlated to the particular party (e.g., the pronunciation guide isrelative to the pronunciation of the user) from the particular device(e.g., the cellular smartphone, or the user's networked computer back athis house, or a server computer) associated with the particular party(e.g., that stores information regarding the particular party, e.g., theuser).

Referring again to FIG. 8G, operation 604 may further include operation870 depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows adaptation data partly based on previous adaptation data derivedfrom one or more previous particular party speech interactions receivingmodule 870 receiving adaptation data (e.g., the phoneme pronunciationguide) correlated to the particular party (e.g., the user) that is atleast partly based on previous adaptation data (e.g., a previous phonemepronunciation guide) derived at least in part from one or more previousspeech interactions of the particular party (e.g., the previous phonemepronunciation guide is at least partly based on phoneme pronunciationsdetected in a previous speech interaction of the particular party).

Referring again to FIG. 8G, operation 868 may include operation 872depicting requesting adaptation data related to one or more vocabularywords from the particular device associated with the particular party.For example, FIG. 3 shows particular party-correlated adaptation datarelated to one or more vocabulary words requesting module 372 requestingadaptation data (e.g., a word confidence factor lookup table, e.g., alookup table for the confidence factor required to accept recognition ofa particular word) related to one or more vocabulary words (e.g.,particular words have a particular confidence factor, e.g., “yes” and“no” may use a low confidence factor since they are not easily confused,but city names (e.g., destinations, such as what might be used at anairline ticket terminal) may require a higher confidence factor in orderto be accepted, depending on the particular user and the level ofdistinctness of their speech) from the particular device (e.g., asmartphone) associated with the particular party (e.g., associated by athird party as belonging to the user).

Referring now to FIG. 8H, operation 868 may include operation 874depicting requesting adaptation data regarding one or more vocabularywords associated with the target device from the particular deviceassociated with the particular party. For example, FIG. 3 showsparticular party-correlated adaptation data regarding one or more targetdevice vocabulary words requesting module 374 requesting adaptation data(e.g., a word pronunciation guide) regarding one or more vocabularywords (e.g., a numeric pronunciation guide with pronunciations fornumbers like “twenty,” “three,” zero,” and “one hundred”) associatedwith the target device (e.g., the target device requests numeric speechinput, e.g., a banking terminal) from the particular device (e.g., asmartphone) associated with the particular party (e.g., the user).

Referring again to FIG. 8H, operation 874 may include operation 876depicting requesting adaptation data regarding one or more vocabularywords used to command the target device from the particular deviceassociated with the particular party. For example, FIG. 3 showsparticular party-correlated adaptation data regarding one or more targetdevice command vocabulary words requesting module 376 requestingadaptation data (e.g., pronunciations of words commonly mispronounced orpronounced strangely by the user) regarding one or more vocabulary words(e.g., “play Pearl Jam,” and “increase volume”) used to command thetarget device (e.g., the sound system of a motor vehicle) from theparticular device (e.g., the smart-key used to start the car, which canalso transmit, receive, and store data) associated with the particularparty (e.g., the driver).

Referring again to FIG. 8H, operation 874 may include operation 878depicting requesting adaptation data regarding one or more vocabularywords used to control the target device from the particular deviceassociated with the particular party. For example, FIG. 3 showsparticular party-correlated adaptation data regarding one or more targetdevice control vocabulary words requesting module 378 requestingadaptation data (e.g., a speech deviation algorithm for words often saidin stressful conditions) regarding one or more vocabulary words (e.g.,“call police,” “activate locking system,” “sound alarm”) used to controlthe target device (e.g., a home security system) from the particulardevice (e.g., a portion of the home security system) associated with theparticular party (e.g., bought by the particular party).

Referring again to FIG. 8H, operation 874 may include operation 880depicting requesting adaptation data regarding one or more vocabularywords used to interact with the target device from the particular deviceassociated with the particular party. For example, FIG. 3 showsparticular party-correlated adaptation data regarding one or more targetdevice interaction vocabulary words requesting module 380 requestingadaptation data (e.g., a word frequency table for a user) regarding oneor more vocabulary words (e.g., for an airline ticket counter, if theuser travels to Boston a lot, the word “Boston” may have a higherfrequency than the word “Austin,” which, while similar sounding, isdifferent, and may aid the target device in deciphering the user'sintent) used to interact with the target device (e.g., an airline ticketcounter) from the particular device (e.g., a smartphone) associated withthe particular party (e.g., the user).

Referring again to FIG. 8H, operation 868 may include operation 882depicting requesting adaptation data regarding one or more vocabularywords commonly used to interact with a type of device receiving theadaptation data from the particular device associated with theparticular party. For example, FIG. 3 shows particular party-correlatedadaptation data regarding one or more target device common interactionwords requesting module 382 requesting adaptation data (e.g., a syllablepronunciation key tied to at least one particular word) regarding one ormore vocabulary words (e.g., the word “play movie”) commonly used tointeract with a type of device (e.g., a speech-enabled media center orcomputer) receiving the adaptation data (e.g., the syllablepronunciation key) from the particular device (e.g., the speechadaptation data box carried by the user) associated with the particularparty (e.g., the user).

Referring again to FIG. 8H, operation 868 may include operation 884depicting requesting adaptation data regarding one or more vocabularywords associated with a type of device receiving the adaptation datafrom the particular device associated with the particular party. Forexample, FIG. 3 shows particular party-correlated adaptation dataregarding one or more target device type associated vocabulary wordsrequesting module 384 requesting adaptation data (e.g., a wordpronunciation guide) regarding one or more vocabulary words (e.g.,requesting only adaptation data related to vocabulary words associatedwith a type of device, and either selecting such specific adaptationdata from the available adaptation data, or letting the device selectthe adaptation data based on the vocabulary words associated with thetype of device) associated with a type of device (e.g., if the type ofdevice is “home entertainment” then the words might be “movie,” “song,”“play,” “stop,” “fast forward,” “rewind,” “pause,” and the like)receiving the adaptation data (e.g., the word pronunciation guide) fromthe particular device (e.g., a universal remote control that stores theadaptation data for many types of devices) associated with theparticular party (e.g., the user).

Referring now to FIG. 8I, operation 870 may include operation 886depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or morespeech interactions of the particular party with at least one priordevice. For example, FIG. 3 shows adaptation data partly based onprevious adaptation data derived from one or more previous particularparty speech interactions with a prior device receiving module 386receiving adaptation data (e.g., a syllable pronunciation guide) that isat least partly based on previous adaptation data (e.g., a previoussyllable pronunciation guide) derived at least in part from one or morespeech interactions of the particular party with at least one priordevice (e.g., a device that the user previously interacted with).

Referring again to FIG. 8I, operation 886 may include operation 888depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device having at least one characteristic in common with thetarget device. For example, FIG. 3 shows adaptation data partly based onprevious adaptation data derived from one or more previous particularparty speech interactions with a common characteristic prior devicereceiving module 388 receiving adaptation data (e.g., a word acceptancealgorithm) that is at least partly based on previous adaptation data(e.g., a previous word acceptance algorithm) derived at least in partfrom one or more previous speech interactions of the particular partywith at least one prior device (e.g., a device that the user haspreviously interacted with, e.g., a clock radio) having at least onecharacteristic (e.g., has a volume control) in common with the targetdevice (e.g., a DVD player).

Referring again to FIG. 8I, operation 888 may include operation 890depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device configured to perform a same function as the target device.For example, FIG. 3 shows adaptation data partly based on previousadaptation data derived from one or more previous particular partyspeech interactions with a same function prior device receiving module390 receiving adaptation data (e.g., a probabilistic word model based onthat particular user and the target device to which the user isinteracting) that is at least partly based on previous adaptation data(e.g., a previous probabilistic word model based on that particular userand a previous device with which the user is interacting) derived atleast in part from one or more previous speech interactions of theparticular party with at least one prior device (e.g., a handheld GPSnavigation system) configured to perform a same function as the targetdevice (e.g., an in-vehicle navigation system).

Referring again to FIG. 8I, operation 890 may include operation 892depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneticket dispensing device that performs a same ticket dispensing functionas the target device, said target device comprising a ticket dispensingdevice. For example, FIG. 3 shows adaptation data partly based onprevious adaptation data derived from one or more previous particularparty speech interactions with a ticket dispenser receiving module 392receiving adaptation data (e.g., an expected response-based algorithm)that is at least partly based on previous adaptation data (e.g., aprevious expected response-based algorithm) derived at least in partfrom one or more previous speech interactions of the particular party(e.g., the user) with at least one ticket dispensing device (e.g., amovie ticket dispensing device) that performs a same ticket dispensingfunction as the target device (e.g., an airplane ticket dispensingdevice), said target device comprising a ticket dispensing device (e.g.,an airplane ticket dispensing device).

Referring now to FIG. 8J, operation 888 may include operation 894depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least onedevice configured to provide a same service as the target device. Forexample, FIG. 3 shows adaptation data partly based on previousadaptation data derived from one or more previous particular partyspeech interactions with a prior device providing a same servicereceiving module 394 receiving adaptation data (e.g., a best-modelselection algorithm) that is at least partly based on previousadaptation data (e.g., a previous best model selection algorithm)derived at least in part from one or more previous speech interactionsof the particular party (e.g., the user) with at least one device (e.g.,an automated insurance claim response system) configured to provide asame service (e.g., automated claim response) as the target device(e.g., a different automated insurance claim response system).

Referring again to FIG. 8J, operation 894 may include operation 896depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least onemedia player configured to play one or more types of media, wherein thetarget device also comprises a media player. For example, FIG. 3 showsadaptation data partly based on previous adaptation data derived fromone or more previous particular party speech interactions with a mediaplayer receiving module 396 receiving adaptation data (e.g., a wordconversion hypothesizer) that is at least partly based on previousadaptation data (e.g., a previous word conversion hypothesizer) derivedat least in part from one or more previous speech interactions of theparticular party (e.g., the user) with at least one media player (e.g.,a Blu-ray player) configured to play one or more types of media (e.g.,Blu-rays, and movies on USB drives), wherein the target device (e.g., aportable MP3 player that is voice-controllable) also comprises a mediaplayer (e.g., the portable MP3 player).

Referring now to FIG. 8K, operation 888 may include operation 898depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device sold by a same entity as the target device. For example,FIG. 3 shows adaptation data partly based on previous adaptation dataderived from one or more previous particular party speech interactionswith a prior device sold by a same entity as the target device receivingmodule 398 receiving adaptation data (e.g., a continuous wordrecognition module) that is at least partly based on previous adaptationdata (e.g., a previous continuous word recognition module) derived atleast in part from one or more previous speech interactions of theparticular party (e.g., the user) with at least one prior device (e.g.,a Samsung television) sold by a same entity (e.g., Samsung) as thetarget device (e.g., a Samsung DVD player).

Referring again to FIG. 8K, operation 898 may include operation 801depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device sold by a same retailer as the target device. For example,FIG. 3 shows adaptation data partly based on previous adaptation dataderived from one or more previous particular party speech interactionswith a prior device sold by a same retailer as the target devicereceiving module 301 receiving adaptation data (e.g., one or moreexample accuracy rates of various speech models previously used, so thata system can pick one that it desires based on accuracy rates andprojected type of usage) that is at least partly based on previousadaptation data (e.g., a previous example accuracy rate) derived atleast in part from one or more previous speech interactions of theparticular party (e.g., the user) with at least one prior device (e.g.,a Sony television with speech recognition) sold by a same retailer(e.g., “Best Buy”) as the target device (e.g., a voice-activatedradio/toaster).

Referring now to FIG. 8L, operation 886 may include operation 803depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device that shares at least one vocabulary word with the targetdevice. For example, FIG. 3 shows adaptation data partly based onprevious adaptation data derived from one or more previous particularparty speech interactions with a prior device sharing at least onevocabulary word receiving module 303 receiving adaptation data (e.g.,data including one or more of locations, login information, credentialinformation, screens for displaying, software needed to obtainadaptation data, a list of hardware compatible with the adaptation data,etc.) that is at least partly based on previous adaptation data (e.g., aprevious version of adaptation data, which may be the same or a subsetof the adaptation data) derived at least in part from one or moreprevious speech interactions of the particular party (e.g., the user)with at least one prior device (e.g., a motor vehicle control system)that shares at least one vocabulary word (e.g., “play music”) with thetarget device (e.g., a voice-controlled Blu-ray player).

Referring again to FIG. 8L, operation 886 may include operation 805depicting receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device that has a larger vocabulary than the target device. Forexample, FIG. 3 shows adaptation data partly based on previousadaptation data derived from one or more previous particular partyspeech interactions with a larger vocabulary prior device receivingmodule 305 receiving adaptation data (e.g., a word acceptance algorithmtailored to the particular party, e.g., the user) that is at leastpartly based on previous adaptation data (e.g., a previous wordacceptance algorithm) derived at least in part from one or more previousspeech interactions of the particular party (e.g., the user) with atleast one prior device (e.g., a motor vehicle control system) that has alarger vocabulary (e.g., the motor vehicle control system has “volumecontrol” and “play” and “stop,” as well as “move seat forward,” and“adjust passenger side mirror”) than the target device (e.g., a mediaplayer, whose vocabulary may include the media playing terms, e.g.,volume control, but not the other terms from the motor vehicle controlsystem vocabulary).

Referring now to FIG. 8M, operation 604 may include operation 807depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon one or more speech interactions of the particular party. For example,FIG. 3 shows particular party-correlated speech interaction basedadaptation data from particular party associated particular devicereceiving module 307 receiving adaptation data (e.g., a probabilisticword model based on the particular user) correlated to the particularparty (e.g., the user), said receiving facilitated by a particulardevice (e.g., a smartphone) associated with the particular party (e.g.,the user's smartphone), wherein the adaptation data (e.g., theprobabilistic word model) is at least partly based on one or more speechinteractions of the particular party (e.g., the smartphone picks up allthe words the user says in the course of its speech interactions, andthe words that are recognized over a particular confidence level arestored as having been spoken, and a probabilistic word model isgenerated and updated based on the frequency of detected words).

Referring again to FIG. 8M, operation 604 may include operation 809depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon one or more particular previous speech interactions of the particularparty selected because of their similarity with one or more expectedfuture speech interactions. For example, FIG. 3 shows particularparty-correlated speech interaction based adaptation data selected basedon previous speech interaction similarity with expected future speechinteraction particular device receiving module 309 receiving adaptationdata (e.g., an expected response-based algorithm) correlated to theparticular party, said receiving facilitated by a particular device(e.g., a computer or server connected to a network and networked with adevice carried by the particular party) associated with the particularparty (e.g., the user), wherein the adaptation data is at least partlybased on one or more particular previous speech interactions of theparticular party (e.g., interactions that were recorded and stored onthe computer) selected because of their similarity with one or moreexpected future speech interactions (e.g., it is determined, eitherthrough explicit input or computational inference, that the user is atan airline ticket counter, so speech interactions involving airlineticket transactions or speech interactions with people involvingairplanes may be selected based on the expectation that a future speechinteraction will be an airline ticket counter interaction).

Referring again to FIG. 8M, operation 809 may include operation 811depicting receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon one or more particular previous speech interactions of the particularparty selected because of at least one specific vocabulary word used insaid particular one or more previous speech interactions. For example,FIG. 3 shows particular party-correlated speech interaction basedadaptation data selected based on use of specific vocabulary wordparticular device receiving module 311 receiving adaptation data (e.g.,a best-model selection algorithm) correlated to the particular party(e.g., the user), said receiving facilitated by a particular device(e.g., a smartkey (e.g., a key that can store, transmit, and receivedata) for a motor vehicle) associated with the particular party (e.g.,the smartkey unlocks a motor vehicle owned by the user), wherein theadaptation data is at least partly based on one or more particularprevious speech interactions of the particular party (e.g., interactionswith other motor vehicle control systems) selected because of at leastone specific vocabulary word (e.g., “seat position”) used in saidparticular one or more previous speech interactions (e.g., the user'sprevious speech interactions with this or other motor vehicles).

Referring again to FIG. 8M, operation 809 may include operation 813depicting receiving adaptation data correlated to the particular partyfrom a device configured to receive speech that is associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data from particular-party speech receiving particular devicereceiving module 313 receiving adaptation data (e.g., a word conversionhypothesizer) correlated to the particular party (e.g., the user) from adevice configured to receive speech (e.g., a tablet with a microphone)that is associated with the particular party (e.g., owned or carried bythe user), wherein the adaptation data is at least partly based onprevious adaptation data (e.g., a previous word conversion hypothesizer)derived at least in part from one or more previous speech interactions(e.g., previous Skype-like video conference calls using the tablet inwhich words are recognized by the particular device) of the particularparty (e.g., the user).

Referring again to FIG. 8M, operation 813 may include operation 815depicting receiving adaptation data correlated to the particular partyfrom a smartphone device configured to receive speech that is associatedwith the particular party, wherein the adaptation data is at leastpartly based on previous adaptation data derived at least in part fromone or more previous speech interactions of the particular party. Forexample, FIG. 3 shows particular party-correlated previous speechinteraction based adaptation data from particular-party speech receivingsmartphone receiving module 315 receiving adaptation data (e.g., acontinuous word recognition module) correlated to the particular party(e.g., the user) from a smartphone device (e.g., a BlackBerry 8800)configured to receive speech (e.g., is capable of receiving speech,recording speech, and making phone calls) that is associated with theparticular party (e.g., carried by the particular party, or licensed tothe particular party in an enterprise setting), wherein the adaptationdata (e.g., the continuous word recognition module) is at least partlybased on previous adaptation data (e.g., a previous continuous wordrecognition module) derived at least in part from one or more previousspeech interactions (e.g., phone calls in which the smartphonerecognizes one or more of the words spoken by the user during theconversation) of the particular party).

Referring now to FIG. 8N, operation 813 may include operation 817depicting receiving adaptation data correlated to the particular partyfrom a device including speech transmission software to receive speechthat is associated with the particular party, wherein the adaptationdata is at least partly based on previous adaptation data derived atleast in part from one or more previous speech interactions of theparticular party. For example, FIG. 3 shows particular party-correlatedprevious speech interaction based adaptation data from particular-partyspeech receiving particular device having speech transmission softwarereceiving module 317 receiving adaptation data (e.g., a pronunciationmodel) correlated to the particular party (e.g., the user) from a deviceincluding speech transmission software (e.g., a tablet with a microphoneand with a videoconferencing software, e.g., Skype, loaded) to receivespeech that is associated with the particular party (e.g., theparticular party speaks into the device to transmit speech), wherein theadaptation data is at least partly based on previous adaptation data(e.g., a previous pronunciation model) derived at least in part from oneor more previous speech interactions (e.g., Skype calls using thedevice) of the particular party (e.g., the user).

Referring again to FIG. 8N, operation 817 may include operation 819depicting receiving adaptation data correlated to the particular partyfrom a tablet device configured to receive speech associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data from particular-party speech receiving tablet receivingmodule 319 receiving adaptation data (e.g., example accuracy rates ofvarious speech models previously used) correlated to the particularparty from a tablet device (e.g., an iPad) configured to receive speech(e.g., has a microphone) associated with the particular party (e.g.,from the user), wherein the adaptation data is at least partly based onprevious adaptation data (e.g., example accuracy rates of various speechmodels used before, but less recently) derived at least in part from oneor more previous speech interactions (e.g., interactions that are pickedup by the microphone of the tablet such that words can be identified,including, but not limited to, voice interactions with the tablet, e.g.,via Apple's voice recognition systems) of the particular party (e.g.,the user).

Referring again to FIG. 8N, operation 817 may include operation 821depicting receiving adaptation data correlated to the particular partyfrom a navigation device configured to receive speech associated withthe particular party, wherein the adaptation data is at least partlybased on previous adaptation data derived at least in part from one ormore previous speech interactions of the particular party. For example,FIG. 3 shows particular party-correlated previous speech interactionbased adaptation data from particular-party speech receiving navigationdevice receiving module 321 receiving adaptation data (e.g., a wordacceptance algorithm) correlated to the particular party (e.g., theuser) from a navigation device (e.g., an onboard motor vehiclenavigation device, or a handheld navigation device used in a car, or asmartphone, tablet, or computer, loaded with navigation software)configured to receive speech associated with the particular party (e.g.,the user interacts with the navigation device by speaking to it),wherein the adaptation data (e.g., the word acceptance algorithm) is atleast partly based on previous adaptation data (e.g., a previous versionof the word acceptance algorithm) derived at least in part from one ormore previous speech interactions of the particular party (e.g.,previous interactions with the navigation device).

Referring again to FIG. 8N, operation 604 may include operation 823depicting receiving adaptation data correlated to the particular partyfrom a device configured to detect speech that is associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party. For example, FIG.3 shows particular party-correlated previous speech interaction basedadaptation data from particular-party speech detecting particular devicereceiving module 323 receiving adaptation data (e.g., a probabilisticword model based on that particular user) correlated to the particularparty (e.g., the user) from a device configured to detect speech (e.g.,has a microphone, e.g., a digital recorder) that is associated with theparticular party (e.g., the user), wherein the adaptation data is atleast partly based on previous adaptation data (e.g., a previousprobabilistic word model) derived at least in part from one or moreprevious speech interactions of the particular party (e.g., the user).

Referring now to FIG. 8P (there is no FIG. 8O to avoid potentialconfusion with the nonexistent Fig. Eighty (80)), operation 604 mayinclude operation 825 depicting receiving adaptation data correlated tothe particular party from a device configured to record speech that isassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.For example, FIG. 3 shows particular party-correlated previous speechinteraction based adaptation data from particular-party speech recordingparticular device receiving module 325 receiving adaptation data (e.g.,an expected response-based algorithm) correlated to the particular party(e.g., the user) from a device (e.g., a digital recorder) that isassociated with the particular party (e.g., owned by the user), whereinthe adaptation data (e.g., the expected response-based algorithm) is atleast partly based on previous adaptation data (e.g., a previousexpected response-based algorithm) derived at least in part from one ormore previous speech interactions of the particular party (e.g., speechinteractions between people and speech input-enabled machines that arerecorded by the digital recorder, e.g., and which may or may not laterbe transmitted to a server, or may be analyzed at a later date by speechanalysis software or hardware).

FIGS. 9A-9C depict various implementations of operation 606, accordingto embodiments. Referring now to FIG. 9A, operation 606 may includeoperation 902 depicting applying the received adaptation data correlatedto the particular party to a speech recognition module of the targetdevice. For example, FIG. 4 shows received adaptation data to speechrecognition module of target device applying module 402 applying thereceived adaptation data (e.g., an expected response-based algorithm)correlated to the particular party (e.g., the user) to a speechrecognition module (e.g., a portion of the target device, eitherhardware or software, that facilitates the processing of speech, e.g.,software that performs filler filtering, or software that calculates ordetermines recognition rate, confidence rate, error rate, or anycombination thereof) of the target device (e.g., an automated tellermachine).

Referring again to FIG. 9A, operation 606 may include operation 904depicting facilitating transmission of the received adaptation data to aspeech recognition module configured to process the speech. For example,FIG. 4 shows transmission of received adaptation data to speechrecognition module configured to process speech facilitating module 404facilitating transmission (e.g., transmitting, or performing some actionwhich assists in eventual transmitting or attempting to transmit) of thereceived adaptation data (e.g., a continuous word recognition module) toa speech recognition module (e.g., programmable hardware module of anairline ticket counter terminal) configured to process the speech (e.g.,perform one or more steps related to the conversion of speech data intodata comprehensible to a processor).

Referring again to FIG. 9A, operation 606 may include operation 906depicting updating a speech recognition module of the target device withthe received adaptation data correlated to the particular party. Forexample, FIG. 4 shows received adaptation data to target device speechrecognition module updating module 406 updating (e.g., determining ifchanges need to be applied, and if so, applying them, or initializing ifno original is found) a speech recognition module (e.g., software forprocessing speech) of the target device (e.g., a navigation system) withthe received adaptation data (e.g., instructions for an adaptationcontrol algorithm) correlated to the particular party (e.g., the user).

Referring again to FIG. 9A, operation 606 may include operation 908depicting modifying a speech recognition module of the target devicewith the received adaptation data. For example, FIG. 4 shows receivedadaptation data to target device speech recognition module modifyingmodule 408 modifying a speech recognition module (e.g., changing atleast one portion of an algorithm used by the speech recognition modulesoftware routine) of the target device (e.g., a voice-commandedcomputer) with the received adaptation data (e.g., a phonemepronunciation guide).

Referring again to FIG. 9A, operation 606 may include operation 910depicting adjusting at least one portion of a speech recognition moduleof the target device with the received adaptation data. For example,FIG. 4 shows received adaptation data to target device speechrecognition module adjusting module 410 adjusting at least one portionof a speech recognition module (e.g., changing at least one setting of aspeech recognition module, e.g., an upper limit number used in at leastone recognition algorithm) of the target device (e.g., an automatedmovie ticket selling machine) with the received adaptation data (e.g., asyllable pronunciation guide).

Referring again to FIG. 9A, operation 606 may include operation 912depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises a pronunciationdictionary. For example, FIG. 4 shows received adaptation data includingpronunciation dictionary to target device speech recognition moduleapplying module 412 applying the received adaptation data (e.g., a wordpronunciation guide) correlated to the particular party (e.g., the user)to a speech recognition module of the target device (e.g., a computerwith speech input capabilities), wherein the received adaptation datacomprises a pronunciation dictionary (e.g., a word pronunciation guide).

Referring again to FIG. 9A, operation 606 may include operation 914depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises a phoneme dictionary. Forexample, FIG. 4 shows received adaptation data including phonemedictionary to target device speech recognition module applying module414 applying the received adaptation data (e.g., the phoneme dictionary)correlated to the particular party (e.g., the user) to a speechrecognition module of the target device (e.g., a tablet device), whereinthe received adaptation data comprises a phoneme dictionary.

Referring now to FIG. 9B, operation 606 may include operation 916depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises a dictionary of one ormore words related to the target device. For example, FIG. 4 showsreceived adaptation data including dictionary of target device relatedwords to target device speech recognition module applying module 416applying the received adaptation data (e.g., a word dictionary, whichwas selected from a larger word dictionary based on the target device,e.g., an Automated Teller Machine) correlated to the particular party(e.g., the word dictionary is based on pronunciations by the particularparty) to a speech recognition module (e.g., software residing insidethe ATM) of the target device (e.g., an automated teller machine),wherein the received adaptation data comprises a dictionary of one ormore words related to the target device (e.g., one or more words relatedto an ATM, e.g., “money”).

Referring again to FIG. 9B, operation 606 may include operation 918depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises a training set of audiodata and corresponding transcript data. For example, FIG. 4 showsreceived adaptation data including training set of audio data andcorresponding transcript data to target device applying module 418applying the received adaptation data (e.g., training data) correlatedto the particular party (e.g., the user) to a speech recognition module(e.g., the hardware and software that are used to receive speech andconvert the speech into a format recognized by a processor) of thetarget device (e.g., a speech input accepting fountain drink orderingmachine), wherein the received adaptation data comprises a training setof audio data and corresponding transcript data (e.g., the adaptationdata includes recordings of the user saying particular words, and atable linking the recordings of those words to the electronicrepresentation of those words, in order to train a device regardingpronunciations by the user, either generally, or with respect to thosespecific words, or both).

Referring again to FIG. 9B, operation 606 may include operation 920depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises one or more weightings ofone or more words. For example, FIG. 4 shows received adaptation dataincluding one or more word weightings data to target device applyingmodule 420 applying the received adaptation data (e.g., word weightingdata) correlated to the particular party (e.g., the user) to a speechrecognition module of the target device (e.g., an automated telephonecall routing system), wherein the received adaptation data comprises oneor more weightings of one or more words (e.g., for a credit card companyhotline, the word “stolen” might get a higher weight than the words“tuna fish”).

Referring again to FIG. 9B, operation 606 may include operation 922depicting applying the received adaptation data correlated to theparticular party to a speech recognition module of the target device,wherein the received adaptation data comprises probability informationof one or more words. For example, FIG. 4 shows received adaptation dataincluding one or more words probability information to target deviceapplying module 422 applying the received adaptation data (e.g.,probability information) correlated to the particular party (e.g., theuser) to a speech recognition module of the target device (e.g., aportable navigation system), wherein the received adaptation datacomprises probability information of one or more words (e.g., a wordincludes a probability of how often that word shows up in aconversation).

Referring again to FIG. 9B, operation 606 may include operation 924depicting processing the received adaptation data for further use in aspeech recognition module exterior to the target device. For example,FIG. 4 shows received adaptation data processing for exterior speechrecognition module usage processing module 424 processing the receivedadaptation data (e.g., a phoneme pronunciation guide) for further use ina speech recognition module (e.g., a device that acts as an intermediaryspeech processing device, or speech transmitting or relaying device,with processing not required) exterior to the target device (e.g., thespeech recognition module might be inside a device carried by the user,and the target device may be one or more terminals that the user wantsto interact with).

Referring now to FIG. 9C, operation 606 may include operation 926depicting modifying an accepted vocabulary of a speech recognitionmodule of the target device based on the received adaptation datacorrelated to the particular party. For example, FIG. 4 shows acceptedvocabulary of speech recognition module of target device modifyingmodule 426 modifying an accepted vocabulary (e.g., changing or adding tothe words that are recognized) of a speech recognition module of thetarget device (e.g., an airline ticket dispensing terminal) based on thereceived adaptation data (e.g., instructions to modify or change thevocabulary) correlated to the particular party (e.g., the user).

Referring again to FIG. 9C, operation 926 may include operation 928depicting reducing the accepted vocabulary of a speech recognitionmodule of the target device based on the received adaptation datacorrelated to the particular party. For example, FIG. 4 shows acceptedvocabulary of speech recognition module of target device reducing module428 reducing the accepted vocabulary (e.g., changing or subtracting fromthe words that are recognized) of a speech recognition module of thetarget device (e.g., a motor vehicle control system) based on thereceived adaptation data (e.g., a limited list of words to accept)correlated to the particular party (e.g., the user).

Referring again to FIG. 9C, operation 926 may include operation 930depicting removing one or more particular words from the acceptedvocabulary of a speech recognition module of the target device based onthe received adaptation data correlated to the particular party. Forexample, FIG. 4 shows accepted vocabulary of speech recognition moduleof target device removing module 430 removing one or more particularwords from the accepted vocabulary (e.g., removing a word that is notrelevant or that the user does not use) of a speech recognition moduleof the target device (e.g., a speech-controlled DVD player) based on thereceived adaptation data correlated to the particular party (e.g., theuser).

FIGS. 10A-10C depict various implementations of operation 608, accordingto embodiments. Referring to FIG. 10A, operation 608 may includeoperation 1002 depicting transmitting at least one of the speech fromthe particular party and the applied adaptation data to an interpretingdevice configured to interpret at least a portion of the received speechtransmission. For example, FIG. 5 shows at least one of speech andapplied adaptation data transmitting to interpreting device configuredto interpret at least a portion of speech module 502 transmitting atleast one of the speech from the particular party and the appliedadaptation data (e.g., one or more elements, e.g., a vocabulary, or analgorithm parameter, or a selection criterion, or the entire moduleconfigured to process speech) to an interpreting device (e.g., a deviceconfigured to process the speech, e.g., the end terminal, e.g., the ATMmachine, which receives the applied adaptation data and/or the speechfrom an intermediate, e.g., a device carried by the user) configured tointerpret at least a portion of the received speech transmission.

Referring again to FIG. 10A, operation 608 may include operation 1004depicting interpreting speech from the particular party using a speechrecognition module of the target device to which the received adaptationdata has been applied. For example, FIG. 5 shows speech recognitionmodule of target device particular party speech interpreting usingreceived adaptation data module 504 interpreting speech (e.g.,converting speech into a format recognizable by a processor) from theparticular party (e.g., the user) using a speech recognition module(e.g., hardware or software, or both) of the target device (e.g., aspeech-commandable security system) to which the received adaptationdata (e.g., a word confidence factor lookup table, (e.g., a lookup tablefor the confidence factor required to accept recognition of a particularword)) has been applied.

Referring again to FIG. 10A, operation 608 may include operation 1006depicting converting speech from the particular party into textual datausing a speech recognition module of the target device to which thereceived adaptation data has been applied. For example, FIG. 5 showsspeech recognition module of target device particular party speechconverting into textual data using received adaptation data module 506converting speech from the particular party (e.g., the user) intotextual data (e.g., text data, e.g., data in a text format, e.g., thatcan appear in a program) using a speech recognition module (e.g.,software) of the target device (e.g., a speech input enabled computer)to which the received adaptation data (e.g., pronunciations of wordscommonly mispronounced or pronounced strangely by the user) has beenapplied.

Referring again to FIG. 10A, operation 608 may include operation 1008depicting deciphering speech from the particular party into word datausing a speech recognition module of the target device to which thereceived adaptation data has been applied. For example, FIG. 5 showsspeech recognition module of target device particular party speechdeciphering into word data using received adaptation data module 508deciphering speech from the particular party (e.g., the user) into worddata (e.g., words appearing on the screen) using a speech recognitionmodule of the target device (e.g., a dictation machine that convertsspeech into a text document) to which the received adaptation data(e.g., a discourse marker ignoring algorithm) has been applied.

Referring now to FIG. 10B, operation 608 may include operation 1010depicting carrying out one or more actions based on analysis of speechfrom the particular party using a speech recognition module of thetarget device to which the received adaptation data has been applied.For example, FIG. 5 shows speech analysis based action carrying out bytarget device particular party speech processing using receivedadaptation data module 510 carrying out one or more actions (e.g., “moveseat backwards) based on analysis of speech from the particular party(e.g., the driver of a motor vehicle) using a speech recognition moduleof the target device (e.g., a motor vehicle) to which the receivedadaptation data (e.g., a best-model selection algorithm) has beenapplied.

Referring again to FIG. 10B, operation 1010 may include operation 1012depicting carrying out a bank transaction based on analysis of speechfrom the particular party using the speech recognition module of abanking terminal as the target device to which the received adaptationdata has been applied. For example, FIG. 5 shows speech analysis basedbank transaction carrying out by banking terminal target device usingreceived adaptation data module 512 carrying out a bank transaction(e.g., withdrawing 300 dollars from a checking account) based onanalysis of speech from the particular party (e.g., the user, e.g., theaccount holder) using the speech recognition module of a bankingterminal as the target device to which the received adaptation data(e.g., a word conversion hypothesizer) has been applied.

Referring again to FIG. 10B, operation 1010 may include operation 1014depicting accessing a bank account associated with the particular partybased on analysis of speech from the particular party using the speechrecognition module of a banking terminal as the target device to whichthe received adaptation data has been applied. For example, FIG. 5 showsspeech analysis based bank account accessing by banking terminal targetdevice using received adaptation data module 514 accessing a bankaccount (e.g., checking the balance of a savings account) associatedwith the particular party (e.g., a user's savings account) using thespeech recognition module of a banking terminal as the target device towhich the received adaptation data (e.g., a continuous word recognitionmodule) has been applied.

Referring again to FIG. 10B, operation 1014 may include operation 1016depicting withdrawing money from a bank account associated with theparticular party based on analysis of speech from the particular partyusing the speech recognition module of a banking terminal as the targetdevice to which the received adaptation data has been applied. Forexample, FIG. 5 shows speech analysis based bank account moneywithdrawal by banking terminal target device using received adaptationdata module 516 withdrawing money from a bank account associated withthe particular party (e.g., the user) based on analysis of speech fromthe particular party (e.g., “withdraw 300 dollars from my account”)using the speech recognition module of a banking terminal as the targetdevice to which the received adaptation data has been applied.

Referring again to FIG. 10B, operation 608 may include operation 1018depicting processing speech from the particular party using the speechrecognition module of the target device to which the received adaptationdata has been applied, wherein the target device is a motor vehicle. Forexample, FIG. 5 shows motor vehicle particular party speech processingusing received adaptation data module 518 processing speech from theparticular party (e.g., the user) using the speech recognition module ofthe target device (e.g., the user's motor vehicle) to which the receivedadaptation data (e.g., instructions for an adaptation control algorithm)has been applied, wherein the target device is a motor vehicle (e.g., acar equipped with speech recognition).

Referring again to FIG. 10B, operation 1018 may include operation 1020depicting processing speech from the particular party into one or morecommands to operate the motor vehicle using the speech recognitionmodule of the target device to which the received adaptation data hasbeen applied. For example, FIG. 5 shows motor vehicle particular partyspeech processing into motor vehicle operation command using receivedadaptation data module 520 processing speech from the particular partyinto one or more commands to operate the motor vehicle (e.g., “startengine,” “apply emergency brake”) using the speech recognition module ofthe target device to which the received adaptation data (e.g., a phonemepronunciation guide) has been applied.

Referring now to FIG. 10C, operation 1018 may include operation 1022depicting processing speech from the particular party into one or morecommands to operate a particular system of the motor vehicle using thespeech recognition module of the target device to which the receivedadaptation data has been applied. For example, FIG. 5 shows motorvehicle particular party speech processing into motor vehicle particularsystem operation command using received adaptation data module 522processing speech from the particular party (e.g., the user) into one ormore commands to operate a particular system (e.g., the sound system) ofthe motor vehicle using the speech recognition module of the targetdevice (e.g., the motor vehicle) to which the received adaptation datahas been applied.

Referring again to FIG. 10C, operation 1022 may include operation 1024depicting processing speech from the particular party into one or morecommands to operate one or more of a sound system, a navigation system,a vehicle information system, and an emergency response system of themotor vehicle using the speech recognition module of the target deviceto which the received adaptation data has been applied. For example,FIG. 5 shows motor vehicle particular party speech processing into oneor more motor vehicle systems including sound, navigation, information,and emergency response operation command using received adaptation datamodule 524 processing speech from the particular party (e.g., the user)into one or more commands (e.g., “tell me how much air is in my frontright tire”) to operate one or more of a sound system, a navigationsystem, a vehicle information system, and an emergency response systemof the motor vehicle using the speech recognition module of the targetdevice (e.g., the motor vehicle) to which the received adaptation data(e.g., a syllable pronunciation guide) has been applied.

Referring again to FIG. 10C, operation 1018 may include operation 1026depicting processing speech from the particular party into one or morecommands to change a setting of the motor vehicle using the speechrecognition module of the target device to which the received adaptationdata has been applied. For example, FIG. 5 shows motor vehicleparticular party speech processing into motor vehicle setting changecommand using received adaptation data module 526 processing speech fromthe particular party (e.g., the user) into one or more commands tochange a setting of the motor vehicle (e.g., “set temperature to 68degrees,” “adjust driver side mirror clockwise and up”) using the speechrecognition module of the target device to which the received adaptationdata (e.g., a word pronunciation guide) has been applied.

Referring again to FIG. 10C, operation 1026 may include operation 1028depicting processing speech from the particular party into one or morecommands to change a position of a seat of the motor vehicle using thespeech recognition module of the target device to which the receivedadaptation data has been applied. For example, FIG. 5 shows motorvehicle particular party speech processing into motor vehicle seatposition change command using received adaptation data module 528processing speech from the particular party (e.g., the user) into one ormore commands to change a position of a seat of the motor vehicle usingthe speech recognition module of the target device to which the receivedadaptation data party (e.g., a guide of the pronunciation keys for atleast one word) has been applied.

Referring again to FIG. 10C, operation 608 may include operation 1030depicting applying one or more settings to the target device based onrecognition of the particular party using the speech recognition moduleof the target device to which the received adaptation data has beenapplied. For example, FIG. 5 shows target device setting based onrecognition of particular party using speech recognition module oftarget device applying using received adaptation data module 530applying one or more settings (e.g., a position of seat and mirrors andambient temperature) to the target device (e.g., the motor vehicle)based on recognition of the particular party (e.g., recognizing apassphrase spoken by the particular user) using the speech recognitionmodule of the target device (e.g., a motor vehicle) to which thereceived adaptation data (e.g., a phoneme pronunciation guide) has beenapplied.

Referring now to FIG. 10D, operation 608 may include operation 1032depicting changing a configuration of the target device based onrecognition of the particular party using the speech recognition moduleof the target device to which the received adaptation data has beenapplied. For example, FIG. 5 shows target device configuration changingbased on recognition of particular party using speech recognition moduleof target device module 532 changing a configuration (e.g., changingwhich programs are loaded, or modifying access levels to particularnetwork drives) of the target device (e.g., a computer in an enterprisesetting) based on recognition of the particular party (e.g., recognizinga passphrase, e.g., in conjunction with another identifier, e.g., alogin or a token) using the speech recognition module of the targetdevice to which the received adaptation data (e.g., a word confidencefactor lookup table) has been applied.

Referring again to FIG. 10D, operation 1032 may include operation 1034depicting changing a subtitle language output of the target device basedon recognition of the particular party using the speech recognitionmodule of the target device to which the received adaptation data hasbeen applied, wherein the target device comprises a disc player. Forexample, FIG. 5 shows disc player subtitle language output changingbased on recognition of particular party using speech recognition moduleof target device module 534 changing a subtitle language output (e.g.,from Japanese to Spanish) of the target device (e.g., a Blu-Ray player)based on recognition of the particular party using the speechrecognition module of the target device (e.g., a speech-enabled Blu-Rayplayer) to which the received adaptation data has been applied, whereinthe target device comprises a disc player.

Referring again to FIG. 10D, operation 608 may include operation 1036depicting processing speech from the particular party using the speechrecognition module of the target device to which the received adaptationdata has been applied. For example, FIG. 5 shows target device speechrecognition module particular party speech processing using receivedadaptation data module 536 processing speech from the particular party(e.g., the user) using the speech recognition module of the targetdevice (e.g., the portable navigation system) to which the receivedadaptation data (e.g., a speech deviation algorithm for words often saidin stressful conditions) has been applied.

Referring again to FIG. 10D, operation 608 may include operation 1038depicting deciding whether to modify the adaptation data based on thespeech processed from the particular party by the speech recognitionmodule of the target device to which the received adaptation data hasbeen applied. For example, FIG. 5 shows adaptation data modificationbased on processed speech from particular party deciding module 538deciding whether to modify the adaptation data (e.g., deciding whetherto change the speech deviation algorithm) based on the speech processedfrom the particular party by the speech recognition module of the targetdevice (e.g., the portable navigation system) to which the receivedadaptation data has been applied.

Referring again to FIG. 10D, in some embodiments in which operation 608includes operations 1036 and 1038, operation 608 may further includeoperation 1040 depicting modifying the adaptation data partly based onthe processed speech and partly based on a received information relatedto a result of the speech-facilitated transaction. For example, FIG. 5shows adaptation data modifying partly based on processed speech andpartly based on received information module 540 modifying the adaptationdata (e.g., the speech deviation algorithm) partly based on theprocessed speech and partly based on a received information related to aresult of the speech-facilitated transaction (e.g., a user score ratingthe transaction).

Referring again to FIG. 10D, in some embodiments in which operation 608includes operations 1036 and 1038, operation 608, which may, in someembodiments, also include operation 1040 may further include operation1042 depicting transmitting the modified adaptation data to theparticular device. For example, FIG. 5 shows modified adaptation datatransmitting to particular device module 542 transmitting the modifiedadaptation data (e.g., an updated version of the speech deviationalgorithm) to the particular device (e.g., a smartphone).

Referring again to FIG. 10D, operation 1036 may further includeoperation 1044 depicting determining a confidence level of the speechprocessed from the particular party by the speech recognition module ofthe target device. For example, FIG. 5 shows particular party processedspeech confidence level determining module 544 determining a confidencelevel (e.g., a numeric representation of how accurate the conversionfrom the speech data is estimated to be) of the speech processed fromthe particular party by the speech recognition module of the targetdevice (e.g., an Automated Teller Machine).

Referring again to FIG. 10D, operation 1036 may further includeoperation 1046 depicting modifying the adaptation data based on thedetermined confidence level of the speech processed from the particularparty by the speech recognition module of the target device. Forexample, FIG. 5 shows adaptation data modifying based on determinedconfidence level of processed speech module 546 modifying the adaptationdata (e.g., a pronunciation guide) based on the determined confidencelevel of the speech processed from the particular party by the speechrecognition module of the target device (e.g., if the confidence levelof words is too low, then modifying the pronunciation guide).

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuitry (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuitry, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Video Disk (DVD), a digital tape, a computer memory, etc.; and atransmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

Alternatively or additionally, implementations may include executing aspecial-purpose instruction sequence or invoking circuitry for enabling,triggering, coordinating, requesting, or otherwise causing one or moreoccurrences of virtually any functional operations described herein. Insome variants, operational or other logical descriptions herein may beexpressed as source code and compiled or otherwise invoked as anexecutable instruction sequence. In some contexts, for example,implementations may be provided, in whole or in part, by source code,such as C++, or other code sequences. In other implementations, sourceor other code implementation, using commercially available and/ortechniques in the art, may be compiled//implemented/translated/convertedinto a high-level descriptor language (e.g., initially implementingdescribed technologies in C or C++ programming language and thereafterconverting the programming language implementation into alogic-synthesizable language implementation, a hardware descriptionlanguage implementation, a hardware design simulation implementation,and/or other such similar mode(s) of expression). For example, some orall of a logical expression (e.g., computer programming languageimplementation) may be manifested as a Verilog-type hardware description(e.g., via Hardware Description Language (HDL) and/or Very High SpeedIntegrated Circuit Hardware Descriptor Language (VHDL)) or othercircuitry model which may then be used to create a physicalimplementation having hardware (e.g., an Application Specific IntegratedCircuit). Those skilled in the art will recognize how to obtain,configure, and optimize suitable transmission or computational elements,material supplies, actuators, or other structures in light of theseteachings.

In a general sense, those skilled in the art will recognize that thevarious aspects described herein which can be implemented, individuallyand/or collectively, by a wide range of hardware, software, firmware, orany combination thereof can be viewed as being composed of various typesof “electrical circuitry.” Consequently, as used herein “electricalcircuitry” includes, but is not limited to, electrical circuitry havingat least one discrete electrical circuit, electrical circuitry having atleast one integrated circuit, electrical circuitry having at least oneapplication specific integrated circuit, electrical circuitry forming ageneral purpose computing device configured by a computer program (e.g.,a general purpose computer configured by a computer program which atleast partially carries out processes and/or devices described herein,or a microprocessor configured by a computer program which at leastpartially carries out processes and/or devices described herein),electrical circuitry forming a memory device (e.g., forms of randomaccess memory), and/or electrical circuitry forming a communicationsdevice (e.g., a modem, communications switch, or optical-electricalequipment). Those having skill in the art will recognize that thesubject matter described herein may be implemented in an analog ordigital fashion or some combination thereof.

Those having skill in the art will recognize that it is common withinthe art to describe devices and/or processes in the fashion set forthherein, and thereafter use engineering practices to integrate suchdescribed devices and/or processes into data processing systems. Thatis, at least a portion of the devices and/or processes described hereincan be integrated into a data processing system via a reasonable amountof experimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

Those skilled in the art will recognize that it is common within the artto implement devices and/or processes and/or systems, and thereafter useengineering and/or other practices to integrate such implemented devicesand/or processes and/or systems into more comprehensive devices and/orprocesses and/or systems. That is, at least a portion of the devicesand/or processes and/or systems described herein can be integrated intoother devices and/or processes and/or systems via a reasonable amount ofexperimentation. Those having skill in the art will recognize thatexamples of such other devices and/or processes and/or systems mightinclude—as appropriate to context and application—all or part of devicesand/or processes and/or systems of (a) an air conveyance (e.g., anairplane, rocket, helicopter, etc.), (b) a ground conveyance (e.g., acar, truck, locomotive, tank, armored personnel carrier, etc.), (c) abuilding (e.g., a home, warehouse, office, etc.), (d) an appliance(e.g., a refrigerator, a washing machine, a dryer, etc.), (e) acommunications system (e.g., a networked system, a telephone system, aVoice over IP system, etc.), (f) a business entity (e.g., an InternetService Provider (ISP) entity such as Comcast Cable, Qwest, SouthwesternBell, etc.), or (g) a wired/wireless services entity (e.g., Sprint,Cingular, Nextel, etc.), etc.

In certain cases, use of a system or method may occur in a territoryeven if components are located outside the territory. For example, in adistributed computing context, use of a distributed computing system mayoccur in a territory even though parts of the system may be locatedoutside of the territory (e.g., relay, server, processor, signal-bearingmedium, transmitting computer, receiving computer, etc. located outsidethe territory)

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermediate components. Likewise, any two componentsso associated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “capable of being operably coupled”, to each other to achievethe desired functionality. Specific examples of operably coupled includebut are not limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

Those skilled in the art will recognize that at least a portion of thedevices and/or processes described herein can be integrated into a dataprocessing system. Those having skill in the art will recognize that adata processing system generally includes one or more of a system unithousing, a video display device, memory such as volatile or non-volatilememory, processors such as microprocessors or digital signal processors,computational entities such as operating systems, drivers, graphicaluser interfaces, and applications programs, one or more interactiondevices (e.g., a touch pad, a touch screen, an antenna, etc.), and/orcontrol systems including feedback loops and control motors (e.g.,feedback for sensing position and/or velocity; control motors for movingand/or adjusting components and/or quantities). A data processing systemmay be implemented utilizing suitable commercially available components,such as those typically found in data computing/communication and/ornetwork computing/communication systems

While particular aspects of the present subject matter described hereinhave been shown and described, it will be apparent to those skilled inthe art that, based upon the teachings herein, changes and modificationsmay be made without departing from the subject matter described hereinand its broader aspects and, therefore, the appended claims are toencompass within their scope all such changes and modifications as arewithin the true spirit and scope of the subject matter described herein.Furthermore, it is to be understood that the invention is defined by theappended claims.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to inventions containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations.

In addition, even if a specific number of an introduced claim recitationis explicitly recited, those skilled in the art will recognize that suchrecitation should typically be interpreted to mean at least the recitednumber (e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.).

In those instances where a convention analogous to “at least one of A,B, or C, etc.” is used, in general such a construction is intended inthe sense one having skill in the art would understand the convention(e.g., “a system having at least one of A, B, or C” would include butnot be limited to systems that have A alone, B alone, C alone, A and Btogether, A and C together, B and C together, and/or A, B, and Ctogether, etc.). It will be further understood by those within the artthat virtually any disjunctive word and/or phrase presenting two or morealternative terms, whether in the description, claims, or drawings,should be understood to contemplate the possibilities of including oneof the terms, either of the terms, or both terms. For example, thephrase “A or B” will be understood to include the possibilities of “A”or “B” or “A and B.”

With respect to the appended claims, those skilled in the art willappreciate that recited operations therein may generally be performed inany order. In addition, although various operational flows are presentedin a sequence(s), it should be understood that the various operationsmay be performed in other orders than those that are illustrated, or maybe performed concurrently. Examples of such alternate orderings mayinclude overlapping, interleaved, interrupted, reordered, incremental,preparatory, supplemental, simultaneous, reverse, or other variantorderings, unless context dictates otherwise. Furthermore, terms like“responsive to,” “related to,” or other past-tense adjectives aregenerally not intended to exclude such variants, unless context dictatesotherwise.

Those skilled in the art will appreciate that the foregoing specificexemplary processes and/or devices and/or technologies arerepresentative of more general processes and/or devices and/ortechnologies taught elsewhere herein, such as in the claims filedherewith and/or elsewhere in the present application.

1. A computationally-implemented method, comprising: receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device; receiving adaptation datacorrelated to the particular party, said receiving facilitated by aparticular device associated with the particular party, wherein theadaptation data is at least partly based on previous adaptation dataderived at least in part from one or more previous speech interactionsof the particular party; applying the received adaptation datacorrelated to the particular party to the target device; and processingspeech from the particular party using the target device to which thereceived adaptation data has been applied.
 2. Thecomputationally-implemented method of claim 1, wherein said receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device comprises: receiving indication ofinitiation of a transaction in which the particular party interacts withthe target device at least partly using speech.
 3. (canceled) 4.(canceled)
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 12. Thecomputationally-implemented method of claim 1, wherein said receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device comprises: receiving indicationthat the particular party is speaking to the target device. 13.(canceled)
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 17. Thecomputationally-implemented method of claim 1, wherein said receivingindication of initiation of a speech-facilitated transaction between aparticular party and a target device comprises: detecting an executionof at least one machine instruction that is configured to facilitatecommunication with the particular party through a speech-facilitatedtransaction.
 18. The computationally-implemented method of claim 1,wherein said receiving adaptation data correlated to the particularparty, said receiving facilitated by a particular device associated withthe particular party, wherein the adaptation data is at least partlybased on previous adaptation data derived at least in part from one ormore previous speech interactions of the particular party comprises:receiving adaptation data comprising speech characteristics of theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.19. (canceled)
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 25. The computationally-implemented method ofclaim 1, wherein said receiving adaptation data correlated to theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular partycomprises: receiving data comprising authorization to receive adaptationdata correlated to the particular party, from a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular party.26. The computationally-implemented method of claim 1, wherein saidreceiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party comprises:receiving data comprising instructions for obtaining adaptation datacorrelated to the particular party, from a particular device associatedwith the particular party, wherein the adaptation data is at leastpartly based on previous adaptation data derived at least in part fromone or more previous speech interactions of the particular party. 27.(canceled)
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 34. The computationally-implemented methodof claim 1, wherein said receiving adaptation data correlated to theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular partycomprises: receiving adaptation data correlated to the particular partyfrom a particular device configured to allow the particular party to login, wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party.
 35. (canceled) 36.(canceled)
 37. (canceled)
 38. (canceled)
 39. Thecomputationally-implemented method of claim 1, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party comprises: receivingadaptation data correlated to the particular party from a particulardevice positioned closer to the particular party than other devices,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party.
 40. (canceled)
 41. Thecomputationally-implemented method of claim 1, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party comprises: receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party with one or more devicesrelated to the target device.
 42. The computationally-implemented methodof claim 1, wherein said receiving adaptation data correlated to theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular partycomprises: receiving adaptation data correlated to the particular party,said receiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with devices usingan intersecting vocabulary as the target device.
 43. (canceled)
 44. Thecomputationally-implemented method of claim 1, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party comprises: receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions with one or more devices configured to carry outsimilar functions as the target device.
 45. (canceled)
 46. Thecomputationally-implemented method of claim 1, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party comprises: receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions with one or more devices that previously carried outa same function as the target device is configured to carry out. 47.(canceled)
 48. The computationally-implemented method of claim 1,wherein said receiving adaptation data correlated to the particularparty, said receiving facilitated by a particular device associated withthe particular party, wherein the adaptation data is at least partlybased on previous adaptation data derived at least in part from one ormore previous speech interactions of the particular party comprises:receiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or morespeech interactions with the particular device.
 49. (canceled)
 50. Thecomputationally-implemented method of claim 1, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party comprises: receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party, wherein said adaptationdata is correlated to one or more vocabulary words.
 51. Thecomputationally-implemented method of claim 50, wherein said receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party, wherein said adaptationdata is correlated to one or more vocabulary words comprises: receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party, wherein said adaptationdata is correlated to one or more vocabulary words used by the targetdevice.
 52. The computationally-implemented method of claim 1, whereinsaid receiving adaptation data correlated to the particular party, saidreceiving facilitated by a particular device associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party comprises:requesting adaptation data correlated to the particular party from theparticular device associated with the particular party; and receivingadaptation data that is at least partly based on previous adaptationdata derived at least in part from one or more previous speechinteractions of the particular party.
 53. (canceled)
 54. Thecomputationally-implemented method of claim 52, wherein said requestingadaptation data correlated to the particular party from the particulardevice associated with the particular party comprises: requestingadaptation data regarding one or more vocabulary words associated withthe target device from the particular device associated with theparticular party.
 55. The computationally-implemented method of claim54, wherein said requesting adaptation data regarding one or morevocabulary words associated with the target device from the particulardevice associated with the particular party comprises: requestingadaptation data regarding one or more vocabulary words used to commandthe target device from the particular device associated with theparticular party.
 56. The computationally-implemented method of claim54, wherein said requesting adaptation data regarding one or morevocabulary words associated with the target device from the particulardevice associated with the particular party comprises: requestingadaptation data regarding one or more vocabulary words used to controlthe target device from the particular device associated with theparticular party.
 57. (canceled)
 58. The computationally-implementedmethod of claim 52, wherein said requesting adaptation data correlatedto the particular party from the particular device associated with theparticular party comprises: requesting adaptation data regarding one ormore vocabulary words commonly used to interact with a type of devicereceiving the adaptation data from the particular device associated withthe particular party.
 59. (canceled)
 60. The computationally-implementedmethod of claim 52, wherein said receiving adaptation data that is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular partycomprises: receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or morespeech interactions of the particular party with at least one priordevice.
 61. The computationally-implemented method of claim 60, whereinsaid receiving adaptation data that is at least partly based on previousadaptation data derived at least in part from one or more speechinteractions of the particular party with at least one prior devicecomprises: receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device having at least one characteristic in common with thetarget device.
 62. The computationally-implemented method of claim 61,wherein said receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device having at least one characteristic in common with thetarget device comprises: receiving adaptation data that is at leastpartly based on previous adaptation data derived at least in part fromone or more previous speech interactions of the particular party with atleast one prior device configured to perform a same function as thetarget device.
 63. The computationally-implemented method of claim 62,wherein said receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device configured to perform a same function as the target devicecomprises: receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneticket dispensing device that performs a same ticket dispensing functionas the target device, said target device comprising a ticket dispensingdevice.
 64. The computationally-implemented method of claim 61, whereinsaid receiving adaptation data that is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party with at least one priordevice having at least one characteristic in common with the targetdevice comprises: receiving adaptation data that is at least partlybased on previous adaptation data derived at least in part from one ormore previous speech interactions of the particular party with at leastone device configured to provide a same service as the target device.65. The computationally-implemented method of claim 64, wherein saidreceiving adaptation data that is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party with at least one deviceconfigured to provide a same service as the target device comprises:receiving adaptation data that is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party with at least one mediaplayer configured to play one or more types of media, wherein the targetdevice also comprises a media player.
 66. Thecomputationally-implemented method of claim 61, wherein said receivingadaptation data that is at least partly based on previous adaptationdata derived at least in part from one or more previous speechinteractions of the particular party with at least one prior devicehaving at least one characteristic in common with the target devicecomprises: receiving adaptation data that is at least partly based onprevious adaptation data derived at least in part from one or moreprevious speech interactions of the particular party with at least oneprior device sold by a same entity as the target device.
 67. Thecomputationally-implemented method of claim 66, wherein said receivingadaptation data that is at least partly based on previous adaptationdata derived at least in part from one or more previous speechinteractions of the particular party with at least one prior device soldby a same entity as the target device comprises: receiving adaptationdata that is at least partly based on previous adaptation data derivedat least in part from one or more previous speech interactions of theparticular party with at least one prior device sold by a same retaileras the target device.
 68. (canceled)
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 78. The computationally-implemented methodof claim 1, wherein said receiving adaptation data correlated to theparticular party, said receiving facilitated by a particular deviceassociated with the particular party, wherein the adaptation data is atleast partly based on previous adaptation data derived at least in partfrom one or more previous speech interactions of the particular partycomprises: receiving adaptation data correlated to the particular partyfrom a device configured to detect speech that is associated with theparticular party, wherein the adaptation data is at least partly basedon previous adaptation data derived at least in part from one or moreprevious speech interactions of the particular party.
 79. (canceled) 80.(canceled)
 81. (canceled)
 82. The computationally-implemented method ofclaim 1, wherein said applying the received adaptation data correlatedto the particular party to the target device comprises: updating aspeech recognition module of the target device with the receivedadaptation data correlated to the particular party.
 83. (canceled) 84.The computationally-implemented method of claim 1, wherein said applyingthe received adaptation data correlated to the particular party to thetarget device comprises: adjusting at least one portion of a speechrecognition module of the target device with the received adaptationdata.
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 92. Thecomputationally-implemented method of claim 1, wherein said applying thereceived adaptation data correlated to the particular party to thetarget device comprises: modifying an accepted vocabulary of a speechrecognition module of the target device based on the received adaptationdata correlated to the particular party.
 93. Thecomputationally-implemented method of claim 92, wherein said modifyingan accepted vocabulary of a speech recognition module of the targetdevice based on the received adaptation data correlated to theparticular party comprises: reducing the accepted vocabulary of a speechrecognition module of the target device based on the received adaptationdata correlated to the particular party.
 94. Thecomputationally-implemented method of claim 92, wherein said modifyingan accepted vocabulary of a speech recognition module of the targetdevice based on the received adaptation data correlated to theparticular party comprises: removing one or more particular words fromthe accepted vocabulary of a speech recognition module of the targetdevice based on the received adaptation data correlated to theparticular party.
 95. (canceled)
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 99. The computationally-implemented method of claim 1,wherein said processing speech from the particular party using thetarget device to which the received adaptation data has been appliedcomprises: carrying out one or more actions based on analysis of speechfrom the particular party using a speech recognition module of thetarget device to which the received adaptation data has been applied.100. (canceled)
 101. (canceled)
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 103. (canceled) 104.(canceled)
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 109. The computationally-implemented method of claim 1,wherein said processing speech from the particular party using thetarget device to which the received adaptation data has been appliedcomprises: applying one or more settings to the target device based onrecognition of the particular party using the speech recognition moduleof the target device to which the received adaptation data has beenapplied.
 110. The computationally-implemented method of claim 1, whereinsaid processing speech from the particular party using the target deviceto which the received adaptation data has been applied comprises:changing a configuration of the target device based on recognition ofthe particular party using the speech recognition module of the targetdevice to which the received adaptation data has been applied.
 111. Thecomputationally-implemented method of claim 110, wherein said changing aconfiguration of the target device based on recognition of theparticular party using the speech recognition module of the targetdevice to which the received adaptation data has been applied comprises:changing a subtitle language output of the target device based onrecognition of the particular party using the speech recognition moduleof the target device to which the received adaptation data has beenapplied, wherein the target device comprises a disc player.
 112. Thecomputationally-implemented method of claim 1, wherein said processingspeech from the particular party using the target device to which thereceived adaptation data has been applied comprises: processing speechfrom the particular party using the speech recognition module of thetarget device to which the received adaptation data has been applied;and deciding whether to modify the adaptation data based on the speechprocessed from the particular party by the speech recognition module ofthe target device to which the received adaptation data has beenapplied.
 113. (canceled)
 114. The computationally-implemented method ofclaim 1, wherein said processing speech from the particular party usingthe target device to which the received adaptation data has been appliedfurther comprises: transmitting the modified adaptation data to theparticular device.
 115. The computationally-implemented method of claim112, wherein said deciding whether to modify the adaptation data basedon the speech processed from the particular party by the speechrecognition module of the target device to which the received adaptationdata has been applied comprises: determining a confidence level of thespeech processed from the particular party by the speech recognitionmodule of the target device; and modifying the adaptation data based onthe determined confidence level of the speech processed from theparticular party by the speech recognition module of the target device.116. A computationally-implemented system, comprising: means forreceiving indication of initiation of a speech-facilitated transactionbetween a particular party and a target device; means for receivingadaptation data correlated to the particular party, said receivingfacilitated by a particular device associated with the particular party,wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party; means for applying thereceived adaptation data correlated to the particular party to thetarget device; and means for processing speech from the particular partyusing the target device to which the received adaptation data has beenapplied. 117-230. (canceled)
 231. A computational language-defineddevice comprising: one or more interchained physical machines ordered toreceive indication of initiation of a speech-facilitated transactionbetween a particular party and a target device; one or more interchainedphysical machines ordered to receive adaptation data correlated to theparticular party from a particular device associated with the particularparty, wherein the adaptation data is at least partly based on previousadaptation data derived at least in part from one or more previousspeech interactions of the particular party; one or more interchainedphysical machines ordered to apply the received adaptation datacorrelated to the particular party to a speech recognition module of thetarget device; and one or more interchained physical machines ordered toprocess speech from the particular party using the speech recognitionmodule of the target device to which the received adaptation data hasbeen applied.